&EPA
United States
Environmental Protection
Agency
Environmental Research
Laboratory
Athens GA 30605
EPA-600/5-79-009
August 1979
Research and Development
Costs and Water
Quality Impacts of
Reducing Agricultural
Nonpoint Source
Pollution
An Analysis
Methodology
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RESEARCH REPORTING SERIES
Research reports of the Office of Research and Development, U.S. Environmental
Protection Agency, have been grouped into nine series. These nine broad cate-
gories were established to facilitate further development and application of en-
vironmental technology. Elimination of traditional grouping was consciously
planned to foster technology transfer and a maximum interface in related fields.
The nine series are:
1. Environmental Health Effects Research
2. Environmental Protection Technology
3. Ecological Research
4. Environmental Monitoring
5. Socioeconomic Environmental Studies
6. Scientific and Technical Assessment Reports (STAR)
7. Interagency Energy-Environment Research and Development
8. "Special" Reports
9. Miscellaneous Reports
This report has been assigned to the SOCIOECONOMIC ENVIRONMENTAL
STUDIES series. This series includes research on environmental management,
economic analysis, ecological impacts, comprehensive planning and fore-
casting, and analysis methodologies. Included are tools for determining varying
impacts of alternative policies; analyses of environmental planning techniques
at the regional, state, and local levels; and approaches to measuring environ-
mental quality perceptions, as well as analysis of ecological and economic im-
pacts of environmental protection measures. Such topics as urban form, industrial
mix, growth policies, control, and organizational structure are discussed in terms
of optimal environmental performance These interdisciplinary studies and sys-
tems analyses are presented in forms varying from quantitative relational analyses
to management and policy-oriented reports.
This document is available to the public through the National Technical Informa-
tion Service, Springfield, Virginia 22161.
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EPA-600/5-79-009
August 1979
COSTS AND WATER QUALITY IMPACTS OF REDUCING
AGRICULTURAL NONPOINT SOURCE POLLUTION
An Analysis Methodology
by
Meta Systems, Inc.
Cambridge, Massachusetts 02138
Grant No. R805036-01-0
Project Officer
Thomas E. Waddell
Technology Development and Applications Branch
Environmental Research Laboratory
Athens, Georgia 30605
ENVIRONMENTAL RESEARCH LABORATORY
OFFICE OF RESEARCH AND DEVELOPMENT
U.S. ENVIRONMENTAL PROTECTION AGENCY
ATHENS, GEORGIA 30605
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DISCLAIMER
This report has been reviewed by the Environmental Research Laboratory,
U.S. Environmental Protection Agency, Athens, Georgia, and approved for
publication. Approval does not signify that the contents necessarily reflect
the views and policies of the U.S. Environmental Protection Agency, nor does
mention of trade names or commercial products constitute endorsement or
recommendation for use.
ii
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FOREWORD
As environmental controls become more costly to implement and the
penalties of judgment errors become more severe, environmental quality
management requires more efficient analytical tools based on greater know-
ledge of the environmental phenomena to be managed. As part of this Labor-
atory's research on the occurrence, movement, transformation, impact, and
control of environmental contaminants, the Technology Development and
Applications Branch develops management and engineering tools to help pol-
lution control officials achieve water quality goals through watershed
management.
Agricultural sources contribute significantly to water pollution
problems in many areas of the United States, but control efforts to reach
water quality goals must recognize the social and economic dimensions of
alternative approaches. This report presents a technique for analyzing
the water quality and economic impacts of alternative activities and non-
point source pollution control policies as a means of identifying best man-
agement practices. The methodology should aid the environmental decision-
maker in establishing balanced nonpoint source pollution control policies.
David W. Duttweiler
Director
Environmental Research Laboratory
Athens, Georgia
ill
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ABSTRACT
This study addresses the problem of analyzing nonpoint source pollution
impacts from agriculture. It was undertaken to determine the feasibility of
developing an analytical method that can be applied to the assessment of con-
trols for reducing nonpoint source pollution from agriculture. The analytical
method developed allows the simultaneous examination of 1) the water quality
impacts of selected agricultural practices and 2) the economic effects that
alternative practices and nonpoint source pollution control policies have on
the farmer. The nonpoint source pollution control problems that the methodo-
logy addresses are limited to those that are amenable to solution by incre-
mental on-farm adjustments for damage reduction.
The proposed methodology includes 1) a farm model, which accepts as exo-
genous inputs alternative agricultural practices available to the farmer and
determines the net revenues resulting from each alternative; 2) a water
quality model, which analyzes the water quality impacts of the selected agri-
cultural practices and which is composed of (a) a watershed model that des-
cribes the pollutants generated by the farming practices and their impact on
river water quality and which evaluates soil loss, and (b) an impoundment
model which evaluates the impoundment water quality effects of the watershed
pollutants; and 3) a qualitative approach for the assessment of the socio-
economic impacts of water quality changes on downstream users. The methodo-
logy is designed to facilitate the comparison of alternative agricultural
practices for the purpose of identifying best management practices (BMP's).
It also may be applied to evaluate government nonpoint source pollution con-
trol policies and the effects of alternative agricultural futures. The
methodology's use for these purposes is evaluated through an illustrative
example based on data from the Black Creek watershed in Northeastern Indiana
and a synthesized downstream impoundment.
It appears that the development of such a methodology for regional-level
planning is feasible and would be of significant value for broad analyses of
large numbers of policy alternatives, including identification of BMP's. How-
ever, the methodology is currently at a preliminary stage of development, and
further refinements are necessary to make it fully operational.
This report was submitted in fulfillment of Grant No. R805036-01-0 by_
Meta Systems Inc under sponsorship of the U.S. Environmental Protection Agency.
This report covers the period August 1, 1977 to September 30, 1978, and work
was completed as of September 30, 1978.
iv
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CONTENTS
Page
Foreword iii
Abstract
Figures
Tables [[[ ....... vii
Acknowledgements .................................................. viii
Section 1 . Introduction .................................... ]_
Section 2 . Conclusions . . ................................ t . t 10
Section 3 . Recommendations .,.,,,,, ................... .,,.,,. 15
Section 4 . Development of a Farm Model ...................... 17
Section 5. Water Quality Impact Analysis .................... 23
Section 6. Use of Farm and Water Quality Models ............. 34
Section 7 . Impacts on Downstream Users ............ . t ........ 53
References .......................................... t ............ 62
Bibliography [[[ 55
Appendices
A. Farm Model .......................................... ..... 77
B. Methods for Predicting Watershed Loadings ............. ... 169
C. Methods for Predicting Impoundment Water Quality ......... 220
D. Water Quality Impact Results: Additional Interpretations
and Sensitivity Analyses ................................ . 292
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FIGURES
Number
Page
1 Methodology for Assessment of Water Quality Impacts and
Socio-Economic Impacts of Agricultural Practices
la Use of Methodology for Assessment of Nonpoint Source
Pollution Control Options Under Alternative Futures.
2 Schematic View of the Watershed/Impoundment Water Quality
Analysis 24
3 Pathways in the Watershed Analysis 28
4 Pathways in the Impoundment Water Quality Analysis 31
5 Comparison of Practices — Lowlands 38
6 Comparison of Practices — Ridge 38
7 Comparison of Practices — Uplands 38
8 Effects of Fertilization Rate on Low Yield and River Nitrogen
Concentrations 47
9 Percent Change of Highest Revenue Factor — Lowland 58
10 Percent Change of Highest Revenue Factor — Ridge 58
11 Percent Change of Highest Revenue Factor — Uplands 59
vi
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TABLES
Number Page
1 Farm Model: Elements of Cost and Revenue 19
2 Major Features of a Selected Set of Farm Practices in the
Black Creek Area.
20
3 Summary of Farm Model Output — 1977 Dollars, in Thousands
(Under Existing Government Policies) 22
4 Net Revenue — 1977 Dollars 35
5 Impact of Farm Practices on Soil Loss 3°
6 Impacts of Farm Practices on Average Annual Concentrations
of Suspended Solids, Nitrogen, and Phosphorus in the River 39
7 Impacts of the Most Erosiv practice (CB-CV) Relative to the
Least Erosive (CBWH-NT) on Various Water Quality Components 40
8 Impacts of Soil Loss Tax (1977 Dollars) (Ridge Farm) 45
9 Net Revenue — 1977 Dollars (Fertilizer Tax Imposed on Nitrogen)... 48
10 Effect of Future Energy Prices (Constant 1977 Dollars) 52
11 Comparison of Methodologies to Measure Water Quality Benefits 54
12 Impacts on Benefit Categories of Water Quality Components 5&
13 Relative Impacts of CBWH Practice on Water Quality Components
and Benefit Categories for the Lowland Soil Type 6°
14 Summary of Relative Impacts of Farming Practices on Benefit
Categories
via
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ACKNOWLEDGEMENTS
We would like to express our appreciation to the Black Creek Project staff
at Purdue University and the Allen County, Indiana Soil and Water Conservation
District, who provided data and insights in the early stages of our work.
Dr. Klaus Alt, of Iowa State University, was also helpful in providing infor-
mation about his methodology for farm economic analysis.
We also acknowledge the efforts of the following people, who provided com-
ments on the draft final report: from Purdue University, Darrel Nelson, Harry
Galloway, and Don Griffith of the Department of Agronomy, Edwin Monke, David
Beasley, and William Miller of the Department of Agricultural Economics, and
James Morrison of the Department of Agricultural Information; Jerome L.
Mahloch of the Corps of Engineers, Vicksburg, Mississippi; Daniel J. Basta of
Resources for the Future, Thomas H. Clarke, Jr., of the Council on Environ-
mental Quality; Robert D. Walker of the University of Illinois, and Thomas
0. Barnwell, Jr., of the U.S. Environmental Protection Agency, Athens, Georgia.
Finally, we would like to thank Mr. Thomas E. Waddell of the Athens
Environmental Research Laboratory for his interest and guidance as project
officer.
viii
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SECTION 1
INTRODUCTION
This study addresses the problem of analyzing nonpoint source pollution
impacts from agriculture. It was undertaken to determine the feasibility
of developing an analytical method that can be applied to the assessment of
controls for reducing nonpoint source pollution from agriculture. It is
widely recognized that the goals of the Water Pollution Control Act Amend-
ments of 1972 will be achieved only if in addition to point source pollution,
nonpoint source pollution is controlled. Authority exists under PL 92-500 and PL-
217 for EPA, in conjunction with individual states to devise policies and ini-
tiate control programs to manage nonpoint source pollution. However, prog-
ress has been slow. Many reasons can be cited, including strong economic
forces that are in conflict with attempts at environmental control, and the
lack of detailed knowledge of physical, chemical, and biological processes
associated with environmental impact of pollutants from nonpoint sources.
Such knowledge is needed to identify pertinent and defensible policies for
analyzing the impacts of agricultural practices.
Agri-environmental problems can be classified in various ways. For this
study we have devised the following classification.
A. Problems in which human or ecosystem health is at issue:
1. those involving residuals generation and transport with
a large array of chemical transformations over a wide
temporal scope and near-linear damage functions, such
as synthetic biocides and toxics;
2. those involving residuals generation and transport of
a few defined elements and non-linear (or threshold)
damage functions, such as nitrates.
B. Problems in which major concern is with aesthetics, recrea-
tion, or other economic impacts:
1. those involving generation and transport of residuals,
such as sediment and nutrients;
2. those involving long-term land productivity, such as
soil loss;
3. those involving spatial diversity, such as monoculture.
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In solving environmental problems, at least two different approaches are
emerging. One involves incremental adjustments at local or regional levels,
while the other is directed to controls at the national level after the
examination of large-scale trade-offs.
It is believed that the environmental problems of types A-2, B-l, B-2,
and B-3 are amenable to solution by incremental approaches based on on-farm
adjustments to reduce damages. In this study we are concerned with the
development of a methodology focused on water pollution problems of types
A-2 and B-l.
Environmental problems of type A-l are not amenable to an incremental-
policy-change approach (i.e., on-farm adjustment). Reasons include:
1) Many synthetic organic chemicals behave largely in an unknown
fashion in nature; their persistence and transport through
food chains and degradation patterns are often not well under-
stood .
2) The risks involved with biocides and toxics may be large and
are uncertain; they involve generations to come as well as
all persons now living. Unintended consequences impact
other crops, fields, times, and populations.
3) The variety of chemicals makes screening of each for safety
difficult. To prove a chemical safe oftens requires years
of testing.
4) Damage is apparently at least linearly related to dose.
Taking these characteristics of biocides and toxics into consideration, it
can be argued that the best approach to their control is one which examines
the broad questions of use, quantity used, exposure, potential adverse col-
lateral consequences, etc., over time and asks if the risks are worth the
economic costs of doing without.
Methods of evaluating the environmental and socio-economic impacts of
agricultural practices should exhibit the following characteristics.
1) Compatibility between data 4) Ease of understanding and
availabilities and requirements communications
2) Robustness against a wide range 5) Usefulness at the appropriate
of alternative agricultural futures planning level
3) Capability of evaluating major 6) Applicability to the full
policy options range of on-farm adaptive
options.
Based on these characteristics, the focus of this study is on farm
decision-making (where crop and technology are decided) and on aggregation
of the individual decisions to a regional level, rather than on modeled
regional level decision-making where these decisions are not made (but often
wished).
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METHODOLOGY DEVELOPMENT
Figure 1 is a flow chart of the proposed methodology and provides a
framework for identifying the analytic techniques employed and the data in-
puts required. It shows 1) the farm model, which accepts alternative agri-
cultural practices available to the farmer as exogenous inputs and determines
the net revenues resulting from each alternative; 2) the water quality model,
which analyzes the water quality impacts of the selected agricultural prac-
tices and which is composed of (a) a watershed model that describes the
pollutants generated by the farming practices and their impact on river water
quality and evaluates soil loss, and (b) an impoundment model that evaluates
the impoundment water quality effects of the watershed pollutants; and 3)
a qualitative approach for the assessment of the socio-economic impacts of
water quality changes on downstream users. Each of these is described in
more detail below and in the following sections. As Figure 1 indicates, the
methodology is designed to facilitate the comparison of alternative agricul-
tural practices for the purpose of identyfing and evaluating best management
practices (BMP's).
Figure la shows how the methodology may be applied to evaluate govern-
ment nonpoint source pollution control policies and the effects of alterna-
tive agricultural futures. The control policies and alternative futures are
inputs to the methodology. Examples illustrating the use of the methodology
for these purposes will be discussed below.
Use of the Illustrative Example
After completing the literature review for this study, it appeared to
us that the most effective way to approach the determination of the feasi-
bility of developing a methodology would be to work through an illustrative
example. The example would allow an assessment of the logic and completeness
of the methodology as well as of the requirements for applying the methodology
in a planning context. In order to minimize required field work and maximize
data available for the example presented in this report, we sought a well-
studied, agricultural watershed with a downstream impoundment. The latter
was considered necessary for an adequate example of an assessment of water
quality impacts in both flowing and impounded waters. We were unable to
find a locality meeting all these requirements; therefore, to implement the
illustrative example, we used the Black Creek watershed in northeastern
Indiana (a U.S. EPA, USDA demonstration project) and synthesized a downstream
impoundment with characteristics typical of those found in the Corn Belt.
Data from impoundments in this region were obtained from the EPA's National
Eutrophication Survey and other sources that permitted regional calibration
of the impoundment water quality models. The work done on the Black Creek
watershed (see Black Creek Study, Final Report, October 1977) provided a good
source for some of the economic, soils, and water quality data needed for
calibration and illustrative application of the methodology.
Agricultural Future Scenarios
The evaluation of environmental control policies for the future requires
analysis against a predicted structure of agriculture. The farmer's decisions
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FIGURE 1:
METHODOLOGY FOR ASSESSMENT OF WATER QUALITY IMPACTS AND SOCIO-
ECONOMIC IMPACTS OF AGRICULTURAL PRACTICES
GowMMnt
Policies
(See Figure 1A)
Economic
Conditions
(See Figure IA)
Watershed
CHoroderUtlc*
• Geo- Morphologic
• Soil Type
•
Jr
i
General
Agricultural
Practice
Alternative*:
• Crop
• Technology
• Etc.
1
1
1
-r*
1
!_
FARM
Farm
Budget -
Model
WATER QUALITY
MODEL
Economic
Evaluation of
* Agricultural H
Practices
1 1
_|
MODEL
.
Net Re
Practi<
• Geo- Morphologic
• Soil Type
• Slope/Length
• Area
• Cllratotogtc
Impoundment
Choree terletlc*
• Morpheme trie
• Hydrologlc
J!
WATER QUALITY MODEL ] "-•
-rl^o.?^ 1 — •
L
Impoundment
Model
Water Quality
Components
Analyzed for
Alternative
Practices
_J
Water Quality
Impacts for
Net Revenue
Ranked
Practices
Soil Lost
Impact
Qualitative
Comparison of
Downstream ~*°
User Benefit
P
Compare Effects of
Practices:
• Farmer Revenue
and Equity
• Water Quality
Impacts
• Downstream
User Benefits
• Soil Loss
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FIGURE 1A: USE OF METHODOLOGY FOR ASSESSMENT OF NONPOINT SOURCE
POLLUTION CONTROL OPTIONS UNDER ALTERNATIVE FUTURES
Alternative
Futures:
• Price of Energy/
Labor/Capital
• Demand for
Animal/Vegetable
Protein
Nonpoint Source
Pollution Control
Policy Options:
• Taxes on Fertilizers/
Biocldes
• Soil Loss
Restrictions
• Management
Practices
• Physical
Structures
Is This
ractlce Acceptable
Under Alternative
Futures
Selection
of Best
Management
Practices
Is This
Policy
Superior To
Other
Policies
f
is This
Icy Acceptable
Under Alternative
Futures
9
INPUTS
-METHODOLOGY APPLICATION
GOVERNMENT INTERPRETATION •
•RESULTS-
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must be analyzed against assumptions regarding the forces driving the agri-
cultural system.
Without attempting to give a complete list of current trends in modern
U.S. agriculture that have led to the current level of water pollution from
agriculture, we present some of the more important ones. Because these forces
are affected by government policies and because they affect nonpoint source
pollution, it is important to include consideration of such trends in a
quantitative framework such as the one proposed here. Some of these broad
national trends can be characterized as follows:
1) tendency toward larger farm units;
2) tendency toward absentee ownership (including corporate
ownership and land speculation);
3) reduction of direct labor inputs because of rising wages,
the growth in organized farm labor, and farm capital
intensification;
4) large capital investments in machinery manufactured by a
few firms;
5) a high degree of market uncertainty because of international
market integration, in addition to weather and other natural
phenomena;
6) emphasis on high yield, single crop farming (intensive mono-
culture) ;
7) increasing utilization of synthetic chemical and nonrenewable
energy use;
8) tendency toward non-integration of livestock rearing acti-
vities, with feed production separated from feedlots;
9) difficulty of new farmer access to farming and of old
farmer adjustments to new conditions because of large
capital stock represented by land, animals, and machinery;
10) concentration of crop marketing and crop distribution
activities in fewer and larger firms, including vertical
integration from farm to retail store;
11) large federal subsidies to agriculture through irrigation,
power, flood control, price supports, and research/develop-
ment extension; and
12) emphasis on product appearance, ease of mechanical handling,
and storability.
Although most of these trends have led to environmental impairment, this
is not to argue that the destructive environmental consequences of U.S. farm-
ing result solely from them. The destruction of the fragile topsoil of
northern New England more than 150 years ago and the great dust bowls of this
century have had long lasting effects. Nor can one conclude from these trends
that their attendant social costs necessarily outweigh the benefits
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associated with increased food production. The point here is that many
national policies and future economic factors influence the range of agri-
cultural practices that will be considered by farmers and hence influence
nonpoint source pollution from agriculture. Because of time and resources,
we have done little on this aspect of methodology development, and this
represents a serious limitation of this study. However, because of the
uncertainties in the future of agriculture and in order for the methodology
to be flexible and operational, it will be necessary in subsequent work to
evaluate agricultural practices across a broad range of alternative futures.
One such future is continuation of the above trends towards a highly concen-
trated food/fiber production system. Some believe that such a future, if
achieved, would be unstable. In Section 6 we discuss briefly other possible
future settings derived from past modifications and extrapolation of current
trends and forces that would influence the environmental impacts from agri-
culture.
Agricultural Practices and Farm Budgets
As the first step in this feasibility study, a farm budget is developed
that assumes the current agricultural structure. A set of agricultural prac-
tices representative of the options available to a farmer in a particular
watershed is selected. In the example presented in this report, 11 practices
(plus two modifications) are selected, and farm budgets are developed for a
uniform farm of 250 acres on each of the three predominant soils in the Black
Creek watershed. Timing of operations and agricultural practices such as
livestock integration and organic farming that are important for both farm
revenues and environmental impacts were not considered because of a lack of
available data and the limited scope of this study.
Water Quality Impacts of Agricultural Practices
To judge the water quality effects of the agricultural practices, the
water quality impacts of each practice/soil combination are analyzed as the sec-
ond step in this example. Watershed and water quality analysis is based on the
assumption of homogeneity of the watershed reflected in the farm level analysis.
This is, of course, illustrative at this preliminary state of methodology
development. Later use of this method would involve evaluation of the aggre-
gate economic and environmental impacts in a heterogenous watershed. Thus
in assessing agricultural practices, a watershed is assumed to be comprised
of a number of fields of equal characteristics. This provides a rough
measure of the unit emissions and water quality impacts — impacts of a
given field/soil type/agricultural practice combination as desired in the
assessment of the impact of agricultural practices. A more realistic evalua-
tion of these practices on a heterogenous watershed (soils, slopes, farm
sizes, and other characteristics) is the next step in the development of a
usuable methodology now that this example of how to proceed with the analysis
of the economic and environmental impacts of various agricultural practices
on a homogeneous watershed has proven feasible.
This evaluation is designed for assessments of long-term average water-
shed responses and water quality impacts. In this analysis the following
water quality parameters are considered.
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r\
1) impoundment sedimentation (kg/m -yr),
a measure of the amount of sediment deposited on the bottom
of the impoundment per year and thus of the impoundment's
useful lifetime;
2) impoundment sediment outflow concentration (kg/m3),
a measure of the amount of sediment suspended in waters
withdrawn from the impoundment;
o
3) river and impoundment nitrogen concentrations (g/m3),
an indication of nitrate levels in the waters;
4) river light extinction coefficient (m"1),
a measure of the resistance to light penetration in the
river due to turbidity and color;
5) impoundment light extinction coefficient (m"1),
a measure of the resistance to light penetration in
the surface waters of the impoundment due to turbidity,
color, and algal growth;
6) impoundment biomass (g chl-a/m3),
a measure of the concentration of suspended algae in the
surface waters of the impoundment during the summers and
thus a measure of the degree of eutrophication.
For each practice, the watershed models predict average loadings of sediment
(sand, silt, and clay fractions), nitrogen, phosphorus, and color as functions
of field/soil characteristics. Transport of water quality components from
the watershed is represented in two phases (dissolved and sediment-bound)
and in two streams (surface runoff and sub-surface drainage). The water
quality models estimate the impact of these loadings on the average concen-
trations of the respective components in the downstream river and impound-
ments. Impoundment water quality response is also assessed with regard to
mean summer transparency and chlorophyll-a concentration, which are important
indices of eutrophication. While water quality impacts are traditionally
assessed with regard to effects of organic loadings (DOD) on dissolved oxygen
levels, such effects are usually critical for discharges of unstable organic
matter under low-flow conditions. The impacts considered in the framework
for analysis developed for this study are more relevant to evaluating the
water quality effects of erosion control practices than are traditional
BOD/DO impacts.
Impact Assessment and Policy Evaluation
The third step involves a comparison of the net revenue of each of the
farm practices with the water quality impacts of each practice. Policies
that would induce those practices that are environmentally advantageous can
then be examined. Policies considered include:
1) conservation practice subsidies or requirements;
2) prohibition of certain cultivation practices?
3) gross soil loss restrictions;
8
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4) gross soil loss taxes;
5) fertilizer limitations or taxes; and
6) manure/legume subsidies or restrictions.
Government policies that are not instituted specifically for environmental
management purposes — for example, price supports — are regarded as sub-
sumed under definitions of alternative agricultural futures.
Socio-Economic Impacts of Non-Farm Users
Finally, a qualitative description of the impacts of different practices
on downstream users is made indicating the direction of the water quality
change in terms of a particular water use and the conflicts among different
users.
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SECTION 2
CONCLUSIONS
Conclusions are presented under three headings: 1) methodology;
2) implementation of a methodology; and 3) data requirements.
METHODOLOGY
1. The following classification appears useful in considering agro-environ-
mental problems:
A. Problems where human or ecosystem health is at issue:
1) those involving residuals generation and transport with an extraordi-
nary array of chemical transformations over a wide temporal scope and
near-linear damage functions, such as synthetic biocides and toxics;
2) those involving residuals generation and transport of a few defined
elements and non-linear (or threshold) damage functions; such as nit-
rates.
B. Problems where major concern is with aesthetics, recreation, or other
economic impacts:
1) those involving residuals generation and transport, such as sediment
and nutrients;
2) those involving long-term land productivity, such as soil loss;
3) those involving spatial diversity, such as monoculture.
Environmental problems of types A-2, B-l, B-2, and B-3 are amenable to solu-
tion by incremental approaches based on on-farm adjustments to reduce damages.
This study addresses the feasibility of developing methodology focused on
water pollution problems of types A-2 and B-l.
Environmental problems of type A-l are not amenable to an incremental
policy change approach (i.e., on-farm adjustment), the reasons being:
• Many synthetic organic chemicals behave largely in an unknown fashion
in nature; their persistence, transport through food chains, and degrada-
tion patterns are often not well-understood.
• The risks involved with biocides and toxics may be large and are uncer-
tain; they involve generations to come as well as all persons now living.
Impacts are on other crops, fields, times, and people than intended.
• The variety of chemicals makes screening of each for safety difficult.
To prove a chemical safe may require years of testing.
10
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• Damage • is apparently linearly related to dose.
As a result of these characteristics of biocides and toxics, it can be argued
that the best approach to their control is one that examines the national
scene and asks if the risks are worth the economic costs of doing without.
Analysis of long-lived residuals might be feasible if data to make the neces-
sary transformations were ever to become available.
2. Methods to evaluate the environmental and socio-economic impacts of agri-
cultural practices should exhibit the following characteristics.
• Compatibility between data avail- • Ease of understanding and communi-
abilities and requirements. eating.
• Robustness against a wide range of • Usefulness at the state level.
alternative agricultural futures , , . ,.,.. . ., ,- -,, *
, .. . , . . • Applicability to the full range of
and agricultural practices. _ ,
on-farm adaptive options.
• Capability of evaluating major
policy options.
3. To develop a useable method for policy analysis by those responsible for
evaluation and implementation of BMP's, it is necessary to focus on farm
decision making (where crops and technology are decided) and on aggregation
of the individual decisions to a regional level, rather than on modeled
regional-level decision making where decisions on practices and crops are not
made. A farm budget approach is thus the appropriate first step in a method-
ology.
4. A broad range of agricultural practices must be evaluated, including live-
stock integration, in order to obtain a full understanding of the range of
environmental impacts and control alternatives.
5. Water quality impacts of different farm practices on different soil types
for sediment, nitrogen, phosphorus, and color can be compared using the
methodology suggested in this report. It is shown that comparison of prac-
tices based on water quality components in some, but not all, cases leads to
results that are in the same direction (but not of the same magnitude) as
comparisons based solely upon gross soil erosion estimates. Erosion control
and water quality improvement strategies are not always similar. In those
cases where the water quality component of greatest importance and gross soil
erosion changes are in the same direction, using soil loss as a proxy measure-
ment for water quality can facilitate the initial evaluation of BMP's.
6. The advantages of using long-term-average time scales for the watershed and
water body response models include:
• simplified analysis;
• reasonable data requirements facilitating use of national, regional, and
local monitoring and experimental data for model calibration and applica-
tion;
• a methodology based in part on existing, well-tested, and widely applied
models (e.g., the Universal Soil Loss Equation);
11
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• flexibility and ease of implementation;
• response models that are easily understood by decision makers;
• response models that are appropriate for assessment of such long-term
water quality problems as sedimentation and eutrophication.
Nevertheless, use of long-term-average time scales precludes direct assess-
ments of:
• watershed and water body responses under extreme meteorologic conditions;
• effects of the timing of various agricultural operations (such as incre-
mental application of fertilizer);
• seasonal variations in water quality induced by normal seasonal varia-
tions in watershed loadings, which may be particularly important in
rivers and impoundments with relatively short hydraulic residence times;
• analysis of the transport and fate of relatively short-lived compounds.
Modification of the methodology to permit assessments of average seasonal res-
ponses would be feasible without losing many of the above-listed advantages of
a long-term-average approach. This is because the USLE and the SCS curve
number models, which form partial bases for the assessment, can be applied to
predict seasonal responses.
7. It appears feasible to develop an analytical framework for the evaluation
of alternative agricultural practices in terms of farm economics and water
quality impacts. The example provided in this report illustrates an evalua-
tion of a homogeneous watershed. This study does not include a general appli-
cation of mixed farm operations on heterogeneous watersheds. It has not
proven feasible to integrate estimation of the socio-economic impacts of
downstream water quality changes (i.e., externalities) into the framework. A
qualitative presentation of the downstream impacts is possible. This presen-
tation provides some insight into the possible upstream-downstream conflicts.
8. The literature does not include any examples of theoretically valid bene-
fit estimation methodologies that are directly applicable to the agricultural
non-point source pollution problem. A number of studies discussed in the
report provide examples of a benefit evaluation that could be applied. But
such a study would require extensive collection of primary data and would
therefore be expensive to implement.
9. At present the method does not take into account planting time, the timing
of fertilizer and biocide applications, or harvesting time, all of which are
important in that they affect both farm revenues and the water quality impacts
of different practices.
10. The methodology can be used to evaluate agricultural practices against
some of the future conditions (e.g., higher energy prices) that might prevail.
It is important to evaluate alternative practices and policies in light of
alternative future scenarios that are depicted as market product price
changes, unit production factors, or other changes.
12
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IMPLEMENTATION/COMPUTATION
1. The farm budget analysis needs to be automated. This would allow inclusion
of more farm practices in the evaluation and testing for sensitivity to the
timing of farming activities.
2. An LP model would be useful in asssisting in the evaluation of policies,
once the watershed and water quality models have been refined.
3. The computations involved in performing the water quality analysis are
relatively simple and straightforward. They can be easily performed with the
aid of a hand calculator or an inexpensive computer program. Sensitivity and
error analyses are facilitated by the latter.
INPUT DATA REQUIREMENTS
1. The relatively simple methodology developed to assess water quality
impacts has been shown to allow use of national, regional, and local data
sources for calibration purposes. Most of the parameter estimates describing
fundamental processes in the watershed and water body would be expected to be
valid at least on a regional basis. The types of localized (e.g., field or
soil-specific) data required to implement the model are frequently available.
2. A preliminary survey of data availability and the results of sensitivity
analyses indicate that improved estimates of the relative impacts of these
agricultural practices could be obtained through more accurate specifications
of the parameter estimates and/or functional forms used to represent the
following relationships or processes in the watershed/water body response
models:
a. sediment delivery, as related to drainage basin characteristics and
sediment texture.
b. sediment texture, as related to soil texture and erosion rate?
c. phosphorus trapping in impoundments, as related to sedimentation and
hydrologic/morphometric characteristics;
d. the origins and dynamics of dissolved color in watersheds and water
bodies;
e. the leaching of dissolved phosphorus from surface crop residues during
snowmelt (this is particularly important for assessments of reduced til-
lage alternatives);
f. seasonal variations in suspended solids and color concentrations in
impoundments;
g. turbidity and light extinction in rivers and impoundments, as related to
suspended solids, color, and algal concentrations;
h. enrichment of surface soils in phosphorus and organic matter as a func-
tion of tillage practice;
13
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i. denitrification in soils, as related to net or total nitrogen input
rates and soil characteristics.
Some of the needs may be satisfied by a more exhaustive search of the litera-
ture and other data sources; others may require initiation of additional moni-
toring and/or experimental work.
3. Data for the farm budget are largely available for conventional farm prac-
tices, but must be collected on a watershed by watershed basis; some of the
data, such as yield response to fertilizer and biocide application and equip-
ment costs for varying farm sizes, are difficult to obtain and/or derive.
Data for a broader set of agricultural practices that include differing farm
and equipment sizes and livestock integration that can have significant
impacts on water quality are difficult to obtain.
4. Data for benefit evaluation are scarce (or do not exist), preventing reli-
able estimation of a relationship between water quality parameters and value
measurements.
5. More data and analysis are required to provide a basis for interpreting the
chlorophyll-a predictions with regard to the possible harmful effects of
increased eutrophication versus the possible beneficial effects of increased
fish production. Development and integration of a model for predicting
impoundment dissolved oxygen levels as a function of external and internal
sources of oxygen demand would be helpful.
14
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SECTION 3
RECOMMENDATIONS
1. Expand the number of agricultural practices evaluated to include, for
example, variations in fertilizer applications, timing of farming activities,
and livestock integration, and develop a classification scheme for the aggre-
gation of farms within a watershed. These improvements would describe the
watershed in more operational (i.e., realistic) terms and therefore provide
greater utility for evaluating alternative BMPs.
2. Expand the types of policies considered and evaluate the sensitivity of
farm net incomes to policy factors such as the amount of tax or level of sub-
sidy.
3. Expand the number and types of alternative future scenarios considered to
include:
a. market product price changes;
b. labor/energy cost changes; and
c. product demand shifts.
4. The water quality assessment should include:
a. Modification of the water quality models to permit the assessment of
seasonal-average watershed and water body responses with regard to all
quality components; transport and fate of relatively stable, toxic com-
pounds, including heavy metals and biocide residues; dissolved oxygen
responses in stratified impoundments; and various instream alternatives
for controlling the impacts of agriculture on water quality, including,
among other things, sedimentation basins, artificial mixing, and reser-
voir operating policies.
b. Additional sensitivity and error analyses to identify critical data
needs within the water quality model framework.
c. A comprehensive search for additional data to satisfy these needs and
to identify processes requiring additional monitoring and/or experi-
mental investigation.
d. Empirical research to further develop data collection methods for esti-
mating one or more of the benefit categories, including human health,
recreation, or aesthetics benefits as related to physical water quality
measurements.
15
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5. Investigate the application of methodologies (such as Paretian analysis)
to a qualitative or non-monetary evaluation of the impacts of agricultural
policies affecting water quality in the context of conflicts among interest
groups.
16
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SECTION 4
DEVELOPMENT OF A FARM MODEL
While major market and regulatory pressures — such as prices, taxes,
subsidies, government regulations — are exerted at a regional or national
level, it is the farmer who responds by choosing his crops and methods of
farming. For this reason the methodology starts with a farm budget.
We assume the farmer desires to maximize net revenues from the agri-
cultural use of his land subject to judgmental constraints that restrict his
willingness to implement drastic changes that imply unusually high risks.
The farm model does not, for example, depict net revenue if the farmer has
income-producing ventures other than his agricultural operations or if, for
example, he shifts from row and field crops to feed lot operations. The
farmer chooses a set of agricultural practices that include:
1) crop rotation;
2) tillage practices;
3) structural erosion and drainage control practices;
4) levels of chemical application.
These choices are represented as inputs to the farm model for the calcula-
tion of a variety of costs associated with operating the farm in the speci-
fied manner. This required developing a data base for the model. The
procedure set forth by Dr. Klaus Alt (See Appendix C, EPA, 1976) was used.1
Each element of cost was updated for 1977 prices and modified where neces-
sary to adapt the model for the Black Creek area.2 The changes were based
on published data for Black Creek and the State of Indiana, opinions of farm
experts in the Black Creek area and at several universities, and information
obtained from farm equipment dealers.
1Several farm models are available (e.g., the Purdue Crop Budget). The Alt
model was selected because it is likely that Appendix C will receive wide-
spread use by agencies involved in the development of BMP's. Dr. Alt was
most helpful in discussing the adjustment of his model.
2A11 estimates for the farm model as adapted to Black Creek and the sources
of information used in that process are presented in Appendix A of this re-
port (unattached, available from EPA). All details associated with the farm
practices, such as types and quantities of fertilizers and biocides, size
and usage of farm implements, including custom hiring and grain drying pro-
cedures, are also contained in Appendix A (Farm Model).
17
-------
Additional inputs to the model specify expected yields and market prices for
each crop. Net revenue is then calculated as follows:
Net Revenue = / Y P A - C
f—( C C C
c=l
where Y = yield per acre of crop c
c
P = price per unit yield for crop c
A = number of acres producing crop c
n = number of crops grown in rotation
C = cost associated with specified farm practice
Table 1 identifies major categories of cost and revenue data incorporated in
the model.
Eleven farm practices available to farmers in the case study area were
selected. These are identified and described in Table 2. Two of the farm
practices for growing corn, soybeans, wheat, and hay in rotation were ex-
panded. This was done to include the option available to the farmer of cus-
tom hiring for planting wheat and meadow and harvesting hay. The custom
hiring alternative was included because it seems unrealistic that a farmer
adopting the farm crop rotation pattern would purchase all the specialized
equipment needed for each crop.
Each practice was evaluated on three soil types characteristic of the
Black Creek case study area. These are termed upland, ridge, and lowland
soils. Different levels of chemical treatment and seeding are associated
with each soil, and crop yields vary. The definitions of the farm practices
and variations associated with soil type were developed by Meta Systems in
consultation with farm experts involved in the Black Creek project at Purdue
University.
Because of the limitation of long-term averages in the water quality
analysis, considerations of timing of agricultural operations such as plant-
ing and harvesting were not included. While the farm budget model, as pre-
sented here, captures the major elements important for assessing the economic
impacts of alternative nonpoint source pollution control policies on the
farmer, further modifications would be necessary before it could be used
effectively in a planning context. Most importantly, the model should be
automated, perhaps employing a revenue-maximizing linear programming model
for policy analysis. This would permit explicit consideration of the timing
of farm operations and other factors, and sensitivity analyses would be easy
to perform. Several automated models, such as the Purdue Crop Budget, are
available and might be adapted to this use. Nevertheless, we caution that
18
-------
TABLE 1: FARM MODEL: ELEMENTS OF COST AND REVENUE
Costs Revenues
Terracing Corn
-Construction -Yield
-Maintenance -Price
Machinery Soybeans
-Fixed Cost -Yield
-Maintenance -Price
Tractor Wheat
-Fixed Cost -Yield
-Maintenance and Repair -Price
Fuel Hay
-Tractor -Yield
-Combine -Price
Seed
-Corn
-Soybeans
-Wheat
-Meadow
Fertilizer
-Nitrogen
-Phosphorus
-Potassium
-Equipment Rental
Biocides
-Herbicides
-Insecticides
Labor
-Direct Labor
-Overhead
Other Costs
-Grain Drying
-Interest on Operating Capital
19
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TABLE 2: MAJOR FEATURES OF A SELECTED SET
IN THE BLACK CREEK AREA
OF FARM PRACTICES
Crops
Tillage Practice
Soil Abbreviated
Conservation Designation
Practice of Farm
Practice
Continuous Corn (CC)
Continuous Corn (CC)
Continuous Corn (CC)
Continuous Corn (CC)
Continuous Corn (CC)
Corn-Soybean
Rotation (CB)
Corn-Soybean
Rotation (CB)
Corn-Soybean
Rotation (CB)
Corn-Soybean
Rotation (CB)
Corn-Soybean-Wheat-
Hay Rotation (CBWH)
Corn- Soybean-Wheat-
Hay Rotation (CBWH)
Conventional tillage,
fall turn plow (CV)
Conventional tillage,
fall turn plow (CV)
Fall shred stalks,
chisel plow, spring
disk (CH)
Fall shred stalks,
chisel plow, spring
disk (CH)
Fall shred, no till
planting (NT)
Conventional tillage,
fall turn plow (CV)
Fall shred, chisel
plow, spring disk (CH)
Fall shred, no-till
planting (NT)
Fall shred, no-till
planting (NT)
Conventional tillage
fall turn plow for corn;
no-till planting for
soybean , wheat , hay
Fall shred stalks, no-
till planting for all
crops, increased use of
herbicides (NT)
without
terracing
with
terracing
without
terracing
with
terracing (T)
without
terracing
without
terracing
without
terracing
without
terracing
with
terracing (T)
without
terracing
without
terracing
cc-cv
CC-CVT
CC-CH
CC-CHT
CC-NT
CB-CV
CB-CH
CB-NT
CB-NTT
CBWH*
CBWH
CBWH* -NT
CBWH-NT
Note*. Entry in parentheses used where needed to distinguish specific compo-
nent of farm practice.
*iridicates farmer-owned equipment for wheat and meadow planting and for hay
mowing, raking, and baling, rather than custom hiring for these operations.
20
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near optimum solutions always be examined with respect to important factors
that may not be incorporated in such a model.
In applying the farm model three fictitious 250-acre farms representative
of conditions in the Black Creek area of Northeast Indiana are considered.
One farm is on the uplands soil, one on ridge soil, and one on lowlands soil
(the properties of these soils are described in Section 3). Table 3 shows
the revenues and costs for each of these farms, assuming uniform adoption of
one of the eleven farm practices in the Black Creek area and existing govern-
ment policies in effect. Highest revenue is achieved with the corn, soybean
cropping pattern and chisel plowing on all three farms. The revenue from the
corn-soybean rotation with conventional tillage is, however, almost as high
(within two percent). These and other results from the farm model are dis-
cussed in Section 6.
The purpose of constructing a farm model is to evaluate agricultural
practices under consideration as Best Management Practices for the impacts on
farm income, water pollution loading, and water quality. Together with the
proposed government policies designed to encourage these practices, the farm
and water quality models should be able to incorporate consideration of at
least the following policies:
1) conservation practice subsidies or requirements;
2) prohibition of certain cultivation practices;
3) gross soil loss restriction;
4) gross soil loss taxes;
5) fertilizer limitations or taxes; and
6) manure/legume subsidies or restrictions.
Investigation of such policies is carried out by 1) modifying the appro-
priate cost or revenue factors in the farm model and recomputing the net
revenues; 2) estimating changes in soil erosion and other water quality im-
pacting parameters; and 3) jointly evaluating the impacts on farm revenues
and water quality. The use of the farm model in this kind of evaluation is
illustrated in Section 6.
In addition to evaluating government policies for pollution control, the
farm model can be used to assess future conditions that may have an impact on
the farmer. Alternative futures can be postulated for government policies
that are not formulated specifically for purposes of environmental management,
such as price subsidies, Alternative futures might depict changes in econo-
mic conditions, such as increasing prices for energy that affect prices of
fuel used on the farm and purchased farm inputs of fertilizer and biocides.
These changes could alter the farmer's choice of crops, tillage practice,
chemical application and hence induce different impacts on water quality. An
example of this application of the farm model is also presented in Section 6.
21
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TABLE 3: SUMMARY OF FARM MODEL OUTPUT ~ 1977 DOLLARS, IN THOUSANDS
(UNDER EXISTING GOVERNMENT POLICIES)
to
ro
FARM
\ PRACTICE
REVENUE\
AND COST \
GROSS
F:EVENUE
A. UPLAND
SOIL
B. RIDGE
SOIL
C. LOWLAND
SOIL
COSTS
A. UPLAND
SOIL
B. RIDGE
SOIL
C. LOWLAND
SOIL
NET
RETURN
A. UPLAND
SOIL
B. RIDGE
SOIL
C. LOWLAND
SOIL
TILLAGE PRACTICES
CORN,
CONVEN-
TIONAL
TILLAGE
(cc-cv)
52.5
65.0
65.0
39.7
11.1
12.7
12.8
23.6
22.3
CORN,
CHISEL
PLOW
(CC-CH)
52.5
65.0
65.0
39.1
10.9
12.1
13.1
21.1
22.9
CORN,
NO-TILL
(CC-NT)
49.9
65.0
52.0
13.0
11.9
15.5
6.9
20.1
6.5
CORN,
SOYBEAN,
CONVEN-
TIONAL
TILLAGE
(CB-CV)
16.3
59.1
59.1
32.9
33.3
31.8
13.5
25.8
24.1
ROTATIONS
CORN
SOYBEAN,
CHISEL
PLOW
(CB-CH)
46.3
59.1
59.1
32.6
33.1
34.5
13.7
26.1
21.6
CORN
SOYBEAN,
NO-TILL
(CB-NT)
11.4
57.9
50.7
32.3
32.7
31.1
12.2
25.1
16.6
CORN,
SOYBEAN,
WHEAT, HAY,
PARTIAL USE
OF HERBICIDES
(CBWH*) (CBWH)
13.0
51.8
19.9
34.4
34.7
35.1
8.5
17.1
11.5
13.0
51.8
49.9
30.6
31.0
31.7
12,4
20.8
18.1
CORN, SOY-
BEAN, WHEAT
HAY,
NO-TILL
(CBWH -NT)(CBWH-NT)
43.0
51.8
49.9
31.2
31.1
35.1
8.8
17.1
13.9
13.0
51.8
19.0
30.3
30.7
31.1
12.8
21.1
17.6
TERRACES
CORN,
CONVEN-
TIONAL
TILLAGE
(CC-CVT)
56,0
68.5
68.5
16.1
18,2
19.1
9.6
20.3
19.1
CORN,
CHISEL
PLOW
(CC-CHT)
56.0
68,5
68.5
15.8
47.6
48.9
10.2
20.9
19.6
CORN,
SOY-
BEAN,
NO-
TIUL
(CB-NTT)
47.1
60.9
53.7
39.9
39.3
40.7
8.6
21.5
13.0
NOTE: COLUMNS MAY NOT ADD DUE TO ROUNDING.
'INDICATES CUSTOM HIRING.
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SECTION 5
WATER QUALITY IMPACT ANALYSIS
INTRODUCTION
The next step in the methodology involves development and use of mathe-
matical models to provide quantitative means of estimating the water quality
impacts of agricultural practices. The development of these models is de-
scribed in detail in unattached Appendices B, C, and D of this report. The
models have been calibrated and applied to assess the changes in water qual-
ity resulting from implementation of 11 farm practices described in Section 4
on each of three field/soil types.
Figure 2 depicts the separation of the water quality analysis into two
major sections:
1) the watershed.or runoff model, which is characterized as generat-
ing different loadings of pollutants depending on agricultural
activities and watershed characteristics.
2) the impoundment, where water quality is dependent upon the type
and quantity of loadings from the watershed and upon impoundment
characteristics.
In this scheme the river is represented as a medium for transporting the pol-
lutant loadings from the watershed to the impoundment. Water quality condi-
tions in the river reflect these loadings, which enter the river in surface
runoff and groundwater base flow and are transported in dissolved and sedi-
ment-bound phases. River water quality is estimated at the point of entry
into the impoundment. Pollutant losses in overland flow and river transport
are aggregated.
The water quality impact analysis includes the following components that
may influence the suitability of waters for beneficial uses:
1) sediment (suspended solids, turbidity);
2) phosphorus;
3) nitrogen;
4) dissolved color;
5) transparency (as influenced by turbidity, color, and algal
growth);
6) algal growth (as measured by chlorophyll-a concentration).
23
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Regional Climatologic
Characteristic s
ffatershed/Soil
Characteristics!
to
Agricultural
Practice
Characteristics
Watershed
Model
Watershed Emissions
and River Water
Quality Characteristics
-suspended solids
-nitrogen
-phosphorus
-color
Impoundment
Model
Impoundment Water
Quality Characteristics
-suspended solids
-nitrogen
-phosphorus
-color
-transparency
-chlorophyll-a
Impoundment Morphometric/
Hydrologic Characteristics
FIGURE 2: SCHEMATIC VIEW OF THE WATERSHED/IMPOUNDMENT WATER QUALITY ANALYSIS
-------
Dissolved oxygen, biocide residues, and biocides are additional water quality
components relevant to the analysis of water quality impacts of agricultural
practices that have not been included in the framework. The model framework
could be adapted to consider dissolved oxygen in stratified impoundments as
influenced by external and internal (photosynthetic) organic matter loadings.
While it is not feasible at this time to model effectively the behavior of
relatively short-lived biocides in the type of framework developed here, con-
sideration of relatively stable biocides and biocide residues may be possible
if and when basic data are available. This possible modification is left for
future work.
The model framework described below should not be viewed as a static or
final form, but as a preliminary and evolving one. Application of sensitivity
and error analysis techniques to the framework will serve to guide future
efforts at refining the methodology. Such efforts would include:
1) obtaining and analyzing addi- 4) considering different time scales
tional data for parameter for averaging? and
estimation; _. . , • **•!.• -,
5) considering additional components.
2) modifying several functional
forms;
3) including additional inter-
actions or mechanisms;
It is apparent that a variety of approaches could be taken in modeling the
behavior of the water quality components in watersheds, rivers, and impound-
ments. Prior to describing the specifics of our approach, it would be appro-
priate to discuss briefly the factors that were considered in selecting or
formulating the models.
BASIS OF MODELING APPROACH
In selecting a modeling approach to the physical land-water interface,
factors related to both defining the overall project goal and performing the
particular analysis have to be considered. Without entering a lengthy dis-
cussion, we would like to briefly document our approach to the model selec-
tion process.
Two points that impact the selection of models are related to the pro-
jects 's goals.
• Applying models in a policy-making context requires availability
of flexible and operational models. Quick computation and
recomputation of the impacts of alternative settings (i.e.,
scenario/policy/practice mix) can only be accomplished if a low-
cost operational tool is available whose input requirements are
limited.
• Given the goals of improving/developing a methodology for evaluat-
ing management practices in terms of water quality impact, it is
necessary to include all the processes and parameters of the
25
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land/water interface related to different farm practices and esti-
mation of those water quality components relevant to existing and
anticipated future standards or criteria.
The premise of our approach is that no single model can adequately cap-
ture the land/water interface (Meta Systems, 1976): aspects of the interface
have to be modeled separately, and the models have to be linked up in a homo-
logous way. Literature exists on problems encountered in developing models,
linking models describing various processes, and making use of various data
bases originally not coordinated for the same purpose. It is therefore impor-
tant to select, develop, or modify models in such a way that they are compat-
ible with one another. Meta Systems (1976) has elaborated factors relevant
to evaluating the appropriateness of models for their inclusion in linkages
of models. These range from justifications of models in terms of the robust-
ness of their quantitative depictions of physical processes to the ease of
directly connecting models. We feel that the following factors have parti-
cular importance for this study.
• Complex simulation programs whose application and execution re-
quire extensive resources (computers, data, manpower, etc.)
usually are not suitable for policy analyses that require a
large amount of separate applications. Should a study demand
predictions of "short-term" conditions, such as runoff and wash-
off, because of single precipitation events, then it is clear
that these types of models would be necessary.
• Complicated models often do not result in reliable and useful
results, considering the difficulties and expense involved in
1) estimating parameters; 2) providing boundary conditions;
3) testing.
• While "complicated" models may provide more "handles" for policy
evaluation and permit substitution of fundamental theory for lack
of empirical data, the theory in this area is rather primitive,
implying that the value of these models is still somewhat low.
• Interpretations of short-term, event-based simulations are more
difficult because they require an arbitrary event definition.
• Given available sources of national and regional data (EPA/NES,
USDA, etc.), we find it desirable to make as much use as pos-
sible of these data in addition to possible local data sources
(generally limited) (Walker, 1977; Reckhow, 1977; Meta Systems, 1976).
To test the feasibility of a framework for economic/physical analysis of
agricultural practices, it was necessary to start with a relatively simple
methodology that yields long-term or seasonal average results; otherwise, the
problems associated with complicated models would dominate the analysis and
detract from the major task. Our conclusions on feasibility rest on this
simple approach. We feel that given currently available data and knowledge
of the relevant physical processes, a framework built from complex models
would not be feasible or useful in a planning context.
26
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METHODS FOR PREDICTING WATERSHED EMISSIONS
1
The methods developed to assess the impacts of agricultural practices on
nonpoint pollutant loadings are of an empirical nature and are concerned with
long-term average emissions, in the spirit of the Universal Soil Loss Equa-
tion (Wischmeier and Smith, 1972). Average export rates of the following
substances are evaluated in surface runoff and in subsurface drainage:
1) Sediment (sand, silt, and clay 3) Dissolved nitrogen; and
fractions); „.
4) Dissolved color.
2) Phosphorus (NH^F/HCl) extractable
particulate and soluble);
The computed concentrations of these components are assumed to be representa-
tive of average water quality conditions in rivers draining the agricultural
watershed. This part of the methodology is appropriate for linking with
downstream models for the purpose of evaluating quality impacts in impounded
waters.
Watershed emissions or loadings are computed as functions of the follow-
ing characteristics:
1) Surface Soil Properties
a. Erodibility (K factor in USLE, Wischmeier and Smith, 1972)
b. Texture (sand, silt, and clay content)
c. Hydrologic Soil Group (SCS/USDA, 1971)
d. NHifF/HCl extractable phosphorus content (in each texture class
e. Phosphorus distribution coefficient (g extractable P/Kg soil)/
(g dissolved P/m3 soil solution)
f. Organic matter content (in each texture class)
2) Watershed/Field Properties
a. Slope
b. Slope length
c. Surface area
d. Total flow (runoff and drainage)
e. Rainfall erosivity (R factor in USLE)
3) Agricultural Practices
a. Cropping factor (C in USLE)
b. Practice factor (P in USLE)
c. Nitrogen and Phosphorus fertilization rates
d. Tillage depth
e. Crop residue management
Pathways involved in the watershed model are depicted in Figure 3. A brief
summary of the essential features of this framework is given below.
Appendix B.
27
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WATERSHED
CHARACTERISTICS
Field Characteristics
Soil Characteristics
Climate
Morphometry
Agricultural
Practices
Crop Yields
TRANSPORT RATES
Sediment
Runoff
Percolation
TRANSPORT MEDIA
COMPOSITION
Sediment
Runoff
Percolation
Nitrogen Budget
AVERAGE RIVER
WATER QUALITY
AND COMPONENT
LOADINGS
Sediment
Phosphorus
Nitrogen
Color
FIGURE 3: PATHWAYS IN THE WATERSHED ANALYSIS
Gross erosion estimates are based upon the Universal Soil Loss Equation
(USLE), which has been developed by the USDA for use in the soil conserva-
tion area. To make the equation more useful as a tool for evaluating water
quality impacts, explicit consideration is given to sediment texture varia-
tions. Since the finer fractions of soil generally have higher surface
areas per unit mass, they have higher adsorption capacities for various
water quality components. By separately considering the clay, silt, and sand
fractions in surface soil and eroded sediment, differences in the behavior
and transport of these size fractions and their adsorbed pollutants are ex-
plicitly represented, both in the watershed and in the impoundment systems.
Applying a separate delivery ratio for each texture class permits estimation
of sediment and adsorbed pollutant transport to the impoundment.
In each texture class the phosphorus and organic matter contents of
sediment particles are assumed to equal those in the corresponding size
fraction of surface soil. Because of shallower mixing depths, reduced til-
lage methods can cause enrichment of surface soils in nutrients and organic
matter. These dependencies are explicitly considered in the model framework.
Extractable phosphorus contents of the clay, silt, and sand fractions are
computed as functions of the respective background levels, fertilization
rates, and tillage depths. Similarly, organic matter contents are computed
from background levels, crop residue additions, and tillage depths. The
computed compositions and delivery rates of sediment in the various size
fractions are used to estimate the sediment-bound loadings of these compo-
nents.
Flow from the watershed consists of two components: surface runoff and
subsurface drainage. The sum of the two is assumed to be independent of soil
type or agricultural practice. This is essentially equivalent to assuming
that average evapotranspiration rates are independent of these factors.
Surface runoff is estimated based upon region, Hydrologic Soil Group (SCS/
USDA, 1971), and farm practice using methodology developed by Woolhiser
28
-------
(1976, also EPA/USDA, 1975). The latter is based upon hydrologic simulations
using the SCS Curve Number model (SCS/USDA, 1971). Drainage is estimated as
the difference between total flow and surface runoff.
Predictions of surface runoff and drainage are used to estimate the
transport of dissolved phosphorus and color. Linear adsorption isotherms are
employed to estimate 1) the dissolved phosphorus concentration in surface
runoff from the average extractable phosphorus content of eroded sediment,
and 2) the dissolved color concentration in surface runoff from the average
organic matter content of eroded sediment. Dissolved phosphorus and color
concentrations in drainage are assumed to be constant at relatively low
values (0.3 g/m3 and Om"1 , respectively) because they are in equilibrium with
subsurface soils which are deficient in extractable phosphorus and organic
matter.
In addition to the sediment-bound and soluble phosphorus loadings, ex-
plicit consideration is given to the potential for leaching of phosphorus
from surface crop residues during snowmelt periods. Because of frozen soil
conditions, dissolved phosphorus in snowmelt may not equilibrate (i.e., be
adsorbed by) surface soils. Timmons, et al. (1968, 1970) have shown this
component to be potentially important when compared with other soluble phos-
phorus losses from agricultural watersheds. Despite the relative lack of
data in this area, leached residue phosphorus has been included because it
may be important to evaluate the impacts of minimum tillage methods which
tend to create a high potential for such losses by leaving crop residues on
the soil surface.
Because nitrogen is generally more mobile in soil systems than phos-
phorus, estimates of average soluble nitrogen export are based upon mass
balance rather than upon computed soil erosion rates and adsorption chemistry.
The input terms in the mass balance include fixation, fertilization, precipi-
tation, and soil mineralization. The output terms include crop yield,
denitrification, and losses in runoff and drainage. For each soil type and
practice, various data sources are used to estimate the net nitrogen input
rate, which is defined as the total input minus crop yield. For each soil
type, denitrification is estimated as a constant fraction of the net input
rate. The total loss in runoff and drainage is then estimated by difference.
This scheme ignores export of particulate nitrogen, which is assumed to be
not as important as a nutrient source or water quality component (see Appen-
dix B, unattached).
The methodology described above is applicable to a single field or plot
of uniform characteristics. In preliminary assessments of agricultural prac-
tices, a hypothetical watershed is assumed to be comprised of a number of
fields of equal characteristics. This provides a rough measure of the unit
emissions and water quality impacts of a given field/soil type/agricultural
practice combination. The methodology could be applied as well to a hetero-
geneous watershed consisting of a number of areas, each with its own set of
field/soil type/practice specifications. The effects of heterogeneous water-
shed characteristics on practice evaluations and conclusions are considered
29
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higher level questions which would be addressed subsequent to analysis of
homogenous watersheds.
In order to conform to an economic analysis, the watershed model is
calibrated to three different field/soil types which are characteristic of
the Black Creek Watershed, Indiana. A research and demonstration program
sponsored in that watershed by the EPA (Christenson and Wilson, 1976; Lake
and Morrison, 1975) has provided some data necessary for calibrating the
models. On each soil type, the watershed model is calibrated for evaluation
of 11 agricultural practices. Details of the calibrated procedures and
results are discussed in Appendix D.
METHODS FOR PREDICTING IMPOUNDMENT WATER QUALITY2
In tune with the watershed models, the framework developed for assessing
impoundment water quality impacts consists of empirical models which are
designed to predict steady-state, seasonal, or long-term average conditions.
The following water quality components are considered:
1) sediment concentrations and trap- 4) mean summer, Secchi Disc trans-
ping rates parencies
2) phosphorus concentrations and 5) mean summer, epilimnetic chloro-
trapping rates phyll-a concentrations
3) nitrogen concentrations and trap-
ing rates
Models are formulated for each of the above components based upon theoretical
considerations and the results of previous modeling efforts. They are
calibrated and tested empirically using a data base characterizing the beha-
vior of these components in Corn Belt impoundments and compiled from various
sources (EPA/NES, 1975; USDA, 1969; ISBH, 1976; USAGE, 1977).
The sensitivities of the above water quality components are assessed
with respect to annual average input rates, or loadings, of the following
substances:
1) water 4) nitrogen
2) sediment (sand, silt, and clay) 5) dissolved color
3) phosphorus (total soluble and
extractable particulate)
Additional independent variables of importance include mean depth and im-
poundment type (reservoir versus natural lake). The pathways in the
impoundment water quality analysis are summarized in Figure 4. Essential
features are discussed below.
o
See Appendix C
30
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LOADINGS
Color
Sediment
Phosphorus
Nitrogen
Color
OUTFLOW/
EPI LIMNETIC
CONCENTRATIONS
Transparency
Chlorophyll-a
Concentration
IMPOUNDMENT MORPHOMETRIC
AND HYDROLOGIC CHARACTERISTICS
FIGURE 1: PATHWAYS IN THE IMPOUNDMENT WATER QUALITY ANALYSIS
Following the watershed model, the behavior of the sand, silt, and clay
fractions of sediment are modeled separately within the impoundment. A modi-
fication of Bruyne's (1953) empirical curves is used to estimate the trapping
efficiency of sediment in each texture class as a function of mean hydraulic
residence time. Bruyne's curves are represented reasonably well by a model
which assumes a first-order decay process for sediment in a completely-mixed
system. Decay rate parameters for clay and silt are selected to match
Bruyne's lower and upper envelope curves, respectively. The sand decay rate
parameter is selected so that essentially all of the influent sand is trapped.
Total sedimentation rate and outflow suspended solids concentration are esti-
mated as the respective sums over texture classes.
The retention, or trapping, of phosphorus is represented by an empirical
model which is calibrated using data on phosphorus budgets and sedimentation
rates provided for a cross-section of 15 impoundments by the EPA's National
Eutrophication Survey (1975) and the USDA (1969). Data indicate that the
"effective settling velocity" (Vollenweider, 1969) for total phosphorus in
these impoundments is a strong function of sedimentation rate. This suggests
that adsorption/sedimentation reactions represent important phosphorus
removal mechanisms in these impoundments. The settling velocity is also
weakly correlated with mean depth and surface overflow rate. Average outflow
phosphorus concentration is estimated from a steady-state mass balance, based
upon the average inflow concentration and computed trapping efficiency.
31
-------
Average outflow concentrations are related to median, summer concentrations
measured within the impoundments using empirical relationships derived from
50 EPA/NES impoundments in the Corn Belt.
The development of models for nitrogen trapping and outflow concentra-
tion follows that of phosphorus. Data suggest, however, that, unlike phos-
phorus trapping, nitrogen trapping is not significantly dependent upon sedi-
mentation rate. The nitrogen trapping model is calibrated using data from
50 EPA/NES impoundments. These impoundments are considerably less efficient
in trapping nitrogen than in trapping phosphorus. In the 50 impoundments
studied, the average nitrogen and phosphorus retention coefficients are .24
and .44, respectively. This is partially attributed to the fact that average
nitrogen loadings are roughly three times in excess of phosphorus loadings,
relative to algal growth requirements. This conforms to the results of EPA/
NES bioassay studies, which indicate that, given adequate light, algae in
most of these impoundments are phosphorus, as opposed to nitrogen limited.
Based upon data from eight impoundments provided by the Indiana State Board
of Health, Secchi Disc transparency is represented as being inversely propor-
tional to the visible light extinction coefficient in the water column.
Light extinction is attributed to the following: 1) water; 2) dissolved
color; 3) non-algal, suspended solids; and 4) algal suspended solids (repre-
sented by chlorophyll-a concentration). The first term is a constant; the
last three are represented as linear functions of the respective concentra-
tions. These relationships are calibrated using data from the region and
the general literature. Estimates of dissolved color are based upon the
color loadings derived from the watershed model, assuming a first-order decay
mechanism for color within the impoundment. Suspended solids concentrations
are derived directly from the sedimentation model. Mean summer chlorophyll-a
concentrations are estimated using the method described below. The applica-
tion of a seasonal correction factor to the average annual outflow color and
suspended solids concentrations permits estimation of mean summer light
extinction coefficients and Secchi Disc transparencies.
Chlorophyll-a is used as an index of primary production, trophic state,
and, in some systems, fish production. The model developed for predicting
chlorophyll-a levels considers the possible effects of algal growth limita-
tion by light, phosphorus, and/or nitrogen. Expressions for the maximum
biomass levels limited by each of the above factors are based upon steady-
state solutions of theoretical equations describing algal growth in a mixed
surface layer. For a given region and climate the light-limited biomass
level is sensitive to epilimnion depth and the portion of the visible light
extinction coefficient attributed to water, color, and non-algal suspended
solids. The phosphorus- and nitrogen-limited levels are dependent upon
mean summer concentrations of total phosphorus and total nitrogen, respec-
tively, in the epilimnion. These limiting biomass expressions are combined
in an empirical form to allow for simultaneous limitation of algal growth
by more than one factor. The model is calibrated and tested using data from
50 impoundments in the Corn Belt. Analyses of residuals, tests for para-
meter stability, and evaluations of model performance on an independent data
set of 20 impoundments are offered as evidence of model verification.
32
-------
The calibrated impoundment model has been linked with the watershed
model to create a framework for assessing the effects of the 11 different
agricultural practices on each of three soil associations in the watershed.
Additional factors which must be specified for the assessment include total
watershed area, impoundment surface area, and impoundment mean depth. Values
of 200 km2, 5 km2, and 4m, respectively, have been selected as. being typical
of watershed/impoundment configurations in the data set used to develop the
impoundment models. With a total flow rate of .25 m/yr from the watershed,
the hypothetical impoundment has a surface overflow of 10 m/yr and a mean
hydraulic residence time of .4 years.
It should be noted that our evaluations of the relative impact of the
practices on impoundment water quality may be somewhat sensitive to this
choice of a watershed/impoundment configuration. The methodology could be
applied as well to alternative configurations. Because the watershed model
is concerned with long-term average loadings, the analytical framework may
be less valid for application to impoundments with extremely short hydraulic
residence times in which seasonal variations in loading may be important.
33
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SECTION 6
USE OF FARM AND WATER QUALITY MODELS
The results derived in this section are for illustrative purposes and
are based on the analytic processes described in the previous sections. In
presenting the examples, our intent is to show how the joint use of the farm
and water quality models could serve as analytical tools in the development and
evaluation of BMP's. The two models are used to illustrate 1) how agricultural
practices can be evaluated under existing policies and 2) how government poli-
cies could affect the implementation of these practices so that they are con-
ducive to water quality improvements. The evaluation of agricultural practices
under current policies uses the 11 selected farm practices listed in Table 2
(as if they constituted a comprehensive set of alternatives currently avail-
able to farmers) and shows how the practices impact farm revenues and water
quality. These results provide the reference conditions from which alterna-
tive policies can be identified and evaluated. Shifts in policies aimed at
improving water quality can affect farm revenues and may require government
actions such as subsidies, taxes, or restrictions on certain agricultural
practices or farm implements. The policies illustrated in this section con-
cern reduction of soil loss and river nitrogen. Future economic conditions
that affect the farmer — apart from environmental regulations — can also
be incorporated in the evaluation by adjusting the farm model. An example
is presented showing the impacts of increased energy costs.
CURRENT PRACTICES
Table 4 shows the ranking of the 11 selected farm practices in terms of
net revenues for the three farms. The corn-bean-wheat-hay rotation using
all farmer-owned equipment has been dropped from the evaluation in favor of
custom hiring for wheat and meadow planting and hay harvesting. Use of the
farmer-owned equipment option would obscure the merits of the four-crop
rotation alternative. The corn-soybean rotations are most profitable based
on prices chosen for these commodities in the illustration (i.e., corn,
$2.00 per bushel; soybeans, $5.00 per bushel; wheat, $2.50 per bushel; hay,
$60 per ton). The chisel plow tillage method would be selected over conven-
tional tillage with a moldboard plow. The maximum profitability for the
three farms ranges from $26,100 (the ridge farm) to $13,700 (the uplands
farm).
Table 5 ranks the farm practices for the three farms according to soil
loss (gross erosion). For the uplands farm the practice which maximized net
revenue results in an annual soil loss of 15.2 tons per acre. On this farm
losses range from 27.2 tons per acre for corn-soybean rotation with conven-
tional plowing (CB-CV) down to 2.7 tons per acre for corn-soybean-wheat-hay
34
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TABLE 4: NET REVENUE — 1977 DOLLARS
Uplands Farm Ridge Farm
Farm Practice $ Rank $ Rank
Continuous Corn, Conventional Tillage,
without Terracing (CC-CV) 12,800 4 (Tie) 23,600 5
Continuous Corn, Conventional Tillage,
with Terracing* (CC-CVT) 9,600 9 20,300 10
Continuous Corn, Chisel Plowing, with-
out Terracing (CC-CH) 13,400 3 24,100 4
Continuous Corn, Chisel Plowing, with
Terracing (CC-CHT) 10,200 8 20,900 8
Continuous Corn, No-Till Planting,
without Terracing (CC-NT) 6,900 11 20,100 11
Corn-Soybeans, Conventional Tillage,
without Terracing (CB-CV) 13,500 2 25,800 2
Corn-Soybeans, Chisel Plowing, with-
out Terracing (CB-CH) 13,700 1 26,100 1
Corn-Soybeans, No-Till Planting,
without Terracing (CB-NT) 12,200 7 25,100 3
Corn-Soybeans, No-Till Planting, with
Terracing (CB-NTT) 8,600 10 21,500 6
Corn-Soybeans Wheat-Hay, Conventional
Tillage for Corn only, without Terrac- 12,400 6 20,800 9
ing (CBWH)
Corn-Soybeans Wheat-Hay, No-Till
Planting, without Terracing (CBWH-NT) 12,800 4 (Tie) 21,100 7
Lowlands Farm
$ Rank
22,300 4
19,100 6
22,900 3
19,600 5
6,500 11
24,400 2
24,600 1
16,600 9
13,000 10
18,100 7
17,600 8
*PTO Terraces.
-------
TABLE 5: IMPACT OF FARM PRACTICES ON SOIL LOSS
cr>
Farm Practice
Continuous Corn, Conventional Tillage,
without Terracing (CC-CV)
Continuous Corn, Conventional Tillage,
with Terracing (CC-CVT)
Continuous Corn, Chisel Plowing, with-
out Terracing (CC-CH)
Continuous Corn, Chisel Plowing, with
Terracing (CC-CHT)
Continuous Corn, No-Till Planting,
without Terracing (CC-NT)
Corn-Soybeans, Conventional Tillage,
without Terracing (CB-CV)
Corn-Soybeans, Chisel Plowing, with-
out Terracing (CB-CH)
Corn-Soybeans, No-Till Planting, with-
out Terracing (CB-NT)
Corn-Soybeans, No-Till Planting, with
Terracing (CB-NTT)
Corn-Soybeans Wheat-Hay, Conventional
Tillage for Corn only, without Terrac-
ing (CBWH)
Corn- Soybeans Wheat-Hay, No-Till
Planting, without Terracing (CBWH-NT)
Uplands
Tons/
Acre
26.5
18.9
12.0
8.5
7.0
27.2
15.2
11.4
8.1
4.3
2.7
Farm
Rank
10
9
7
5
3
11
8
6
4
2
1
Ridge
Tons/
Acre
9.1
6.5
4.1
3.0
2.4
9.4
5.2
3.9
2.8
1.5
0.9
Farm
Rank
10
9
7
5
3
11
8
6
4
2
1
Lowlands
Tons/
Acre
3.4
2.4
1.6
1.1
0.9
3.5
2.0
1.5
1.0
0.5
0.4
Farm
Rank
10
9
7
5
3
11
8
6
4
2
1
Notes: Soil Loss = Gross Erosion
Highest Rank, 1 = Minimum Soil Loss
-------
rotation with no tillage (CBWH-NT). These soil loss figures refer to gross
erosion rates (before application of delivery ratios). They are proportional,
but not directly applicable, to assessment of receiving water impacts.
For the ridge farm the practice which maximizes annual net revenue
($26,100) results in annual soil loss of 5.2 tons per acre. Soil loss on
the ridge farm ranges from 9.4 tons per acre for conventional tillage on the
corn-soybean rotation (CB-CV) down to 0.9 tons per acre for the no tillage
corn-soybean-wheat-hay rotation (CBWH-NT).
For the lowlands farm the farm practice which maximizes annual net
revenue ($24,600) has an annual soil loss of two tons per acre. Soil losses
on the lowlands farm range from 3.5 tons per acre for the CC-CV and CB-CV
practices down to 0.4 tons per acre for the CBWH-NT farm practice.
Rankings of the farm practices with respect to suspended solids, nitro-
gen, and phosphorus concentrations in the river are shown in Table 6. The
farm practices and their net revenues can be compared with these pollutant
load contributions in the same manner as illustrated above for soil loss.
As discussed in Section 5, in addition to soil loss, six variables
related to water quality were analyzed for the three farms and the 11 farm
practices. The results, together with net revenues, are displayed as three
sets of bar graphs (Figures 5, 6, and 7). A complete listing of the water
quality impacts is presented in Appendix D. The bar graphs are constructed
so that increasing pollutant loads or concentrations are shown by higher
vertical lengths of the bar; for net revenue vertical length increases with
higher returns. The six water quality components displayed and the dimen-
sions used to quantify them are
• Impoundment sedimentation (kg/m2)1
• River nitrogen (g/m3)
• River phosphorus (g/m3)
• River light extinction coefficient (m"1)
• Impoundment light extinction coefficient (m"1)
Q
• Impoundment biomass (g chl-a/nr)
The tables and graphs described above illustrate the types of informa-
tion produced by the proposed methodology for the case in which government
policies are the same as at present. We emphasize that the 11 selected
farm practices form an incomplete set of alternatives actually available
to a farmer; there are many others. There are also interesting options
that do not use synthetic biocides and/or fertilizers. The body of informa-
tion currently available from Indiana sources is not yet adequate to estimate
costs for these options. Nevertheless, estimates are becoming available
from other sources because of the increasing use of such techniques among
large-scale farmers concerned about the risks of synthetic biocides. If
= kilograms; g = grams; m = meters; yr = years.
37
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OJ
oo
NET REVENUE
or
NET REVENUE (K £) 20
RIVER NITROGEN
(g/m'l
RIVER PHOSPHORUS
1
nl
*
RIVER LIGHT EXTINCTION COEFFICIENT
-
10 .
SOIL LOSS ( kg/m2ol
•oterthod- yr)
SEDMENTATION .00
0
MPOUNOMENT LIGHT
EXTINCTION COEFFICIENT 3
(m-l) 2
I
0
.013
IMPOUNDMENT BIOMASS
(g Chlorophyll- A/m») -010
.005
o L
FIGURE 5: COMPARISON OF
PRACTICES — LOWLANDS
FIGURE 6: COMPARISON OF
PRACTICES — RIDGE
FIGURE 7: COMPARISON OF
PRACTICES — UPLANDS
-------
TABLE 6: IMPACTS OF FARM PRACTICES ON AVERAGE ANNUAL CONCENTRATIONS OF SUSPENDED SOLIDS, NITROGEN,
AND PHOSPHORUS IN THE RIVER
Farm Practice
Continuous Corn, Conven-
tional Till., without Ter-
racing (CC-CV)
Continuous Corn, Conven-
tional Till . , with Terrac-
ing (CC-CVT)
Continuous Corn, Chisel
Plow. , without Terracing
(CC-CH)
Continuous Corn, Chisel
Plow., with Terracing (CC-CHT)
Continuous Corn, No-Till
Plant, without Terracing
(CC-NT)
Corn-Soybean , Conventional
Till, without Terracing
(CB-CV)
Corn-Soybean, Chisel Plow.,
without Terracing (CB-CH)
Corn-Soybean, No-Till Plant.,.
without Terracing (CB-NT)
Corn-Soybean, No. Till.
Plant, with Terracing (CB-NTT)
Corn-Soybean-Wheat-Hay, Con-
ventional Till, for Corn onlji
without Terracing (CBWH)
Corn-Soybean-Wheat-Hay, No-
Till Plant. , without Terrac-
ing (CBWH-NT)
Uplands Farm
SS
kg/m R
3.39 10
2.44 9
1.59 7
1.15 5
.94 3
3.47 11
1.98 8
L.51 6
1.09 4
.60 2
.39 1
N
g/m R
12.6 9
11.5 6
12.6 9
11.5 6
15.6 11
9.5 3
9.5 3
10.5 6
9.9 5
6.5 1
6.8 2
P
g/m R
.09 4
.08 2
.10 8
.09 4
.15 11
.09 4
.09 4
.13 10
.12 9
.07 1
.08 2
Ridge Farm
SS
kg/m R
1.11 10
.81 9
.54 7
.39 5
.33 3
1.13 11
.66 8
.51 6
.37 4
.21 2
.14 1
N
g/m R
18.5 9
17.1 7
18.5 9
17.1 7
22.0 11
13.1 3
13.1 3
14.7 6
14.0 5
8.7 1
8.7 1
P
g/m R
.14 8
.12 3
.13 5
.12 3
.16 11
.14 8
.13 5
.14 8
.13 5
.09 2
.08 1
Lowlands Farm
SS
kg/m R
.50 10
.36 9
.23 7
.17 5
.14 3
.51 11
.29 8
.22 6
.16 4
.09 2
.06 1
N
g/m R
11.1 9
10.2 7
11.1 9
10.2 7
16.3 11
7.8 3
7.8 3
9.7 6
9.2 5
5.6 1
5.8 2
P
g/m R
.19 6
.17 3
.19 6
.18 4
.21 11
.19 6
.18 4
.20 10
.19 6
.14 1
.15 2
OJ
10
Notes: SS = Suspended Solids; N = Nitrogen; P = Phosphorus; R = Rank
-------
Oelhafs figures (Oelhaf, 1976) are accepted, that cost of farming without
the use of synthetic chemicals is within 10 to 15 percent of the cost.
Options for which no illustrative calculations were made include: a ful-
ler use of year-round rotations; integrated pest management; and integrated
livestock and cropping operations. Some of these options may contribute to
increased economic and environmental stability. We are convinced that it is
important to evaluate rotation alternatives (and this includes the CBWH farm
practices) and at the same time analyze the role of livestock in the farm
unit.
INTERPRETATION OF WATER QUALITY IMPACTS
As shown in Figures 5, 6, and 7, the water quality impacts of agricul-
tural practices vary with field/soil type, water body (river versus impound-
ment) , and specific pollutant. Use of soil loss alone as the criterion for
farm practice evaluations can lead to erroneous conclusions because of the
importance of various dissolved components in the water and the interactive
effects of different processes (e.g., decay, adsorption/desorption, sedi-
mentation). For illustrative purposes, Table 7 lists the relative impacts of
two farm practices on water quality components in the river and impoundment
for each soil type. Relative impacts are measured as the ratio of the impact
TABLE 7: IMPACTS OF THE MOST EROSIVE PRACTICE (CB-CV) RELATIVE TO THE
LEAST EROSIVE (CBWH-NT) ON VARIOUS WATER QUALITY COMPONENTS
Loading or Concentration
Component* Location Ratio (CB-CV)/(CBWH-NT)
S_oil_Type_
Surface Runoff
Gross Erosion
Suspended Solids Concentration
Suspended Solids Concentration
Sedimentation Rate
Dissolved Nitrogen Concentration
Dissolved Nitrogen Concentration
Dissolved Phosphorus Concentation
Particulate Phosphorus Concentration
Total Phosphorus Concentration
Total Phosphorus Concentration
Dissolved Color Concentration
Dissolved Color Concentration
Light Extinction Coefficient
Light Extinction Coefficient
Light Extinction Coefficient**
Chlorophyll-a Concentration**
Watershed
Watershed
River
Impoundment
Impoundment
River
Impoundment
River
River
River
Impoundment
River
Impoundment
River
Impoundment
Impoundment
Impoundment
Lowland
1.25
10.00
9.22
8.40
9.24
1.35
1.22
.81
7.80
1.28
.88
.87
.87
4.58
1.65
1.25
.90
Ridge
4.92
10.00
8.20
6.31
8.30
1.50
1.26
.64
4.29
1.61
.80
1.67
1.67
7.88
4.77
2.00
.80
Upland
1.76
10.00
8.92
7.80
8.97
1.39
1.22
.49
4.20
1.15
.29
.97
.97
8.56
5.93
3.63
.25
*Annual averages unless otherwise noted.
**Summer averages.
40
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on water quality of the most erosive farm practice (CB-CV) to that of the
least erosive practice (CBWH-NT).
For any given soil type the Universal Soil Loss Equation predicts a ten-
fold difference in the gross erosion rates between the two farm practices.
The effects on gross erosion are, however, attenuated by the selective ero-
sion and transport of finer sediment fractions. Therefore, the ratio of
suspended solids concentrations for the three soil types range from 8.2 to
9.2 in the river and from 6.3 to 8.4 in the impoundment.
Effects of reducing soil erosion are further attenuated in the case of
river particulate phosphorus concentrations, and the ratios range from 4.2 to
7.8. River dissolved phosphorus concentrations are actually lower in the
more erosive case, as indicated by ratios less than 1.0 in Table 7. This
result is attributed to:
1) snowmelt, which leaches dissolved phosphorus from crop residues
on the soil surface in the no-till case; and
2) enrichment of surface soil phosphorus levels caused by the
shallower tillage and fertilizer incorporation depths charac-
teristic of the no-till case.
Increases in dissolved phosphorus produced by the CBWH-NT farm practice par-
tially offset the particulate phosphorus decreases resulting from that prac-
tice. The net result is a 1.2- to 1.6-fold difference in river total
phosphorus concentrations, despite a ten-fold difference in gross erosion
rates. In the outflow of the impoundment the less erosive farm practice
(CBWH-NT) results in higher phosphorus concentrations than the more erosive
one (CB-CV). This reversal of effect is attributed to increased impoundment
phosphorus trapping efficiency due to higher sedimentation rate. This effect
is particularly evident in the relatively steep and phosphorus-deficient up-
land soils.
Variations in dissolved color also do not follow those of soil loss.
Color differences are attributed to differences in 1) runoff, and 2) enriched
levels of organic matter in the surface soil, as influenced by tillage depths.
Light extinction coefficients are inversely related to water trans-
parencies and are influenced by turbidity (suspended solids), dissolved color,
and in summer algal growth. Variations in suspended solids concentrations
are chiefly responsible for the 4.6- to 8.6-fold higher river extinction
coefficient values resulting from the more erosive practice. Because of
selective trapping of coarse suspended solids and color decay within the
impoundment, ratios of annual average impoundment extinction coefficients are
reduced to a range of 1.7 to 5.9 for the various soil types. With the algal
component included, summer extinction coefficient ratios are further reduced
to the 1.2 to 3.6 range.
Use of the less erosive practice results in higher chlorophyll-a concen-
trations in the impoundment, ratios ranging from .25 to .90. This is attri-
buted to 1) higher phosphorus concentrations in the impoundment (as discussed
41
-------
above), and 2) the reduced effect of light-limitation on algal growth which
results when turbidity (suspended solids concentration) is lowered. In the
extreme — the upland case — implementation of the least erosive practice
causes a ten-fold reduction in soil loss, but a four-fold increase in chloro-
phyll-a concentration. Chlorophyll-a increases in the other soil types are
less significant, with ratios ranging from .8 to .9.
These results indicate a possible conflict between the water quality
management goals of controlling sedimentation and of eutrophication using
the types of farm practices evaluated here. Taking into consideration fish
production, higher chlorophyll-a levels could, however, be considered bene-
ficial under certain conditions. Such conditions might include 1) relatively
shallow impoundments without extensive stratification; 2) chlorophyll-a
concentrations sufficiently low so that occasional major fluctuations in
dissolved oxygen (due to algal die-offs and/or respiration during cloudy
periods) do not create lethal conditions; and 3) commercial or recreational
objectives that emphasize quantity rather than quality or species of fish
(i.e., "trash fish" are acceptable). Under these conditions if a model user
were to rank fish production as a higher priority than water quality, there
would be no conflict. Water quality features that are negatively impacted
by algal production — for example, transparency, taste, odor, or in a strati-
fied impoundment dissolved oxygen concentrations in bottom waters — would be
secondary considerations. Additional data and analyses are needed to provide
an adequate basis for interpreting the chlorophyll-a predictions from a bene-
fit point of view. Interpretations would be facilitated by expanding the
impoundment water quality model to permit direct estimation of impoundment
dissolved oxygen concentrations as influenced by both external (watershed)
and internal (photosynthetic) sources of oxygen demand.
With the possible exceptions of phosphorus and eutrophication, control
of soil erosion produces beneficial effects on water quality. Nevertheless,
as demonstrated above, the relative magnitudes of these effects are consider-
ably smaller than indicated by relative soil loss. In addition, effects on
nitrogen concentrations are governed by farm nitrogen budgets rather than
soil loss.
The importance of one pollutant compared to another may also shift from
watershed to watershed and hence influence the selection of those water
quality components of primary importance to the evaluation of the BMP's;
that is, the different pollutants should be ranked on the basis of the sever-
ity of local water quality issues. In assessing BMP's it seems reasonable
to first compare farm practices and their net revenues with respect to the
primary pollutants and then incorporate the other pollutants into the analy-
sis.
To illustrate the linking and application of the farm and water quality
models, soil loss and nitrogen are used as basic measures of water quality
impact in the following discussion. More detailed discussions and interpreta-
tions of the water quality impacts of the practices and soil types are
included in Appendix D.
42
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FARM PRACTICES AND FUTURE POLICIES
The purpose of linking the farm and water quality models is to evaluate
the effects of proposed government policies concerned with agricultural prac-
tices on farm income, water pollution loadings, and water quality. For
illustrative purposes we consider the following policies.
Conservation Practice Subsidies or Requirements
First, let us consider erosion control subsidies for structural improve-
ments. Terraces are an important soil-saving option. Their total annual
cost for our farm of 250 acres is estimated at $6,460, nearly all of which
represents construction costs.2 This is incorporated in Table 4 as a cost
totally borne by the farmer, and as a consequence terracing alternatives look
less attractive than other alternatives.
A 50 percent terracing subsidy, however, brings the net revenues of the
continuous corn chisel plow alternatives on terraced land (CC-CHT) more in
line with the highest non-subsidized practice of corn-soybean chisel plow
(CB-CH) on non-terraced land. Although the corn-soybean chisel plow practice
on terraced land (CB-CHT) was not computed, that alternative would be slightly
more favorable than continuous corn with chisel plowing on terraced land
(CC-CHT) and would presumably be selected with the 50 percent subsidy. Such
a subsidy amounts to $3,230 per 250 acres or about $13 an acre. Soil loss
reduction and cost per unit improvement are3
Soil Loss Cost Per Ton of
Reduction Reduction in Soil Loss
Upland: 7 tons/acre $ 1.90
Ridge: 2 tons/acre $ 6.50
Lowland: 1 ton/acre $13.00
Prohibition of Certain Cultivation Practices
The second class of policies — prohibition of cettain tillage practices
such as conventional plowing — would have no apparent economic impact on the
farms analyzed here, but could reduce soil loss. This assumes, of course,
equal access by a farmer to moldboard and chisel plows. Table 4 directly
indicated the cost impact on the farmer of any required shift in crop prac-
tice by comparing the forbidden maximum revenue alternative to the permitted
maximum revenue alternative.
Comparison of Terracing and Prohibition of Tilling Practices
We may also compare the two policies for reducing soil loss: the $3,230
subsidy per farm; and the prohibition of certain tillage practices. For
example, prohibiting moldboard plowing in favor of chisel plowing for
2See Appendix A, Table A-l for derivation of terrace cost.
3The soil loss estimates shown in Table 5 are rounded to the nearest ton in
this and subsequent examples.
43
-------
continuous corn (CC-CH) or corn-soybean rotations on non-terraced land
(CB-CH) reduces the soil loss as follows:
Continuous Corn Corn-Soybean Rotation
Upland: 15 tons/acre 12 tons/acre
Ridge: 5 tons/acre 4 tons/acre
Lowland: 2 tons/acre 1 ton/acre
The substitution of the plowing implements could be accomplished for less cost
than the terrace subsidy, and major improvements in soil loss could thus be
achieved on the upland farm. If the farmer were subsidized for the acquisi-
tion of a $2,150 chisel plow, the cost would be no more than $350 per year;
this is the yearly fixed cost for the implement. If the farmer liquidated
a moldboard plow as part of a farm implement subsidy package, the cost of
the subsidy program could be reduced. From another view, if we assume that
the value of each ton of soil retained by terracing is judged to be worth the
50 percent subsidy involved (e.g., on the uplands farm this amounts to $1.90
per ton subsidy), then prohibition of moldboard plowing in favor of chisel
plowing on the upland farm is worth approximately $25 per acre for continuous
corn and the corn-soybean rotation. This value is about double the $13 per
acre value implied by the 50 percent terrace subsidy.
Gross Soil Loss Restrictions
Gross soil loss restrictions are sometimes suggested as watershed plan-
ning goals, if not absolute prohibitions. There are numerous ways to apply
such restrictions, but for the purposes of this exposition we consider them
to apply over each acre of a watershed. Such an interpretation maximizes
their impact on costs and on erosion.
Consider, for example, a restriction on gross soil loss of four tons/acre
maximum. This implies the following mandated shifts in cropping activities
to comply with four tons/acre soil loss.
1. For the upland farm the practice with highest net revenue that meets
the soil loss criterion is the corn-soybean-wheat-hay rotation with no til-
lage (CBWH-NT); additional herbicides are used in the spring to kill the
remaining sod before planting corn. (Note that we are considering soil loss
as the primary problem; on other grounds use of biocides would probably be
rejected in favor of mechanical cultivation which would, of course, increase
soil loss to four tons/acre, a bit above the loss expected with the CBWH-NT
farm practice). Net revenue decline is
CB-CH = $13,700
CBWH-NT = $12,800
Decline = $ 900 for 250 acres
Reduction in soil loss is about (15 tons/acre for the CB-CH practice - 3 tons/
acre for the CBWH-NT practice) = 12 tons/acre.
44
-------
2. For ridge soils costs to the farmer are somewhat greater, and soil
loss reductions considerably smaller. The shift is from corn-soybean with
chisel plowing (CB-CH) to corn-soybean with no tillage (CB-NT):
CB-CH = $26,100
CB-NT = $25,100
Decline = $ 1,000 for 250 acres
Reduction in soil loss is only one ton/acre.
3. For the lowlands farm no change from the net revenue maximizing farm
practice (CB-CH) would be necessary to meet gross soil loss restrictions of
four tons/acre.
Gross Soil Loss Taxes
Gross soil loss taxes are a fourth type of policy of interest in control-
ling water pollution. For illustrative purposes a tax of 40 cents per ton
on soil losses is assumed, and economic impacts are measured.
1. For the uplands farm corn-soybean with chisel plowing (CB-CH) is the
net revenue maximizer without tax; soil loss is 15 tons/acre or 3,750 tons/
year for the farm. Tax is $1,500, so the new net revenue is (13,700 - 1,500)
= $12,000. The CBWH-NT practice has a soil loss of three tons/acre or 250
tons/year, so tax is $300 and new net revenue is ($12,800 - $300) = $12,500.
Therefore, net revenues are greater, and the CBWH-NT practice would be chosen.
2. For the ridge farm the impact of a soil loss tax on the revenues from
the 11 practices is shown in Table 8. Minor changes in ranking of the net
revenues occur as a result of the soil loss tax. However, the advantage of
chisel over conventional plowing in terms of dollars net revenue is increased.
TABLE 8: IMPACTS OF SOIL LOSS TAX (1977 DOLLARS)
(RIDGE FARM)
Farm
Practice
CC-CV
CC-CVT
CC-CH
CC-CHT
CC-NT
CB-CV
CB-CH
CB-NT
CB-NTT
CBWH
CBWH-NT
Net Revenue Soil Loss
$ Rank (tons/acre)
23,600
20,300
24,100
20,900
20,100
25,800
26,100
25,100
21,500
20,800
21,100
5
10
4
8
11
2
1
3
6
9
7
9
7
4
3
2
9
5
4
3
1
1
Revenue After
Tax Tax
($.40/ton) $ Rank
900
700
400
300
200
900
500
400
300
100
100
22,700
19,600
23,700
20,600
19,900
24,900
25,600
24,700
21,200
20,700
21,100
5
11
4
9
10
2
1
3
6
8
7
45
-------
3. For the lowlands farm the taxes and impacts would be small for a soil
loss tax because there is little erosion potential with any of the farm prac-
tices.
Fertilizer Limitations or Taxes
A fifth policy type considers a fertilizer tax to reduce over-application
of fertilizer — especially nitrogen. The rationale behind such a tax would
be as follows. Because of the small slope of the fertilizer response curve
in the region of interest (where farmers now operate), a tax can encourage
less fertilizer use with modest declines in crop yield and even smaller re-
ductions in net revenue. However, the effects of reduced nitrogen applica-
tions are magnified as beneficial impacts on water quality because of the
non-linear nature of the water body response to nitrogen. For example, see
Figure 8. To evaluate the implications of a fertilizer tax policy, two
approaches are illustrated. In the first approach a relatively high tax on
nitrogen fertilizer is investigated to determine how changes in farm prac-
tices might be induced and how water quality would be affected. In the sec-
ond approach we show that relatively large reductions in nitrogen use can be
attained with small reductions in yield. A fertilizer tax might be used to
obtain this result without changing the agricultural practice desired by the
farmer.
Nitrogen fertilizer is first assumed to be heavily taxed at $0.07 per
pound, representing a price increase of about 50 percent over the price used
in the reference cases developed in Appendix A. This tax reduces net reve-
nues by a maximum of $3,400 (on the ridge farm) for the farm practice using
the most nitrogen (CC-NT) and by about $900 for the least nitrogen-dependent
practices (corn-soybean-wheat-hay rotations). The corn-soybean chisel plow
farm practice (CB-CH) is still the highest ranking net revenue practice, but
both of the corn-soybean-wheat-hay alternatives have moved up in the ranking,
as shown in Table 9. We can estimate water quality implications from data
presented earlier in Table 6 on river nitrogen concentrations from the
various farm practices. For example, if a sufficiently high fertilizer tax
could be imposed so that net revenues for the corn-soybean-wheat-hay no-till
farm practice (CBWH-NT) were equal to those for the corn-soybean chisel plow
(CB-CH), river nitrogen would be reduced 28 percent for the uplands farm,
34 percent for the ridge farm, and 26 percent for the lowlands farm. This
requires a fertilizer tax of $0.13 per pound (or a 100 percent increase in
the price of nitrogen to the farmer) for the uplands farm. For the ridge
farm the tax required is $0.54 per pound and for the lowlands farm, $0.74
per pound, representing nitrogen price increases to the farmer of 415 percent
and 570 percent respectively.
For the second illustrative case the use of nitrogen is somewhat reduced,
and the farmer continues to select the same agricultural practice as in the
reference case. We have used corn-nitrogen response functions to estimate
the yields for different levels of nitrogen application (see Appendix F,
unattached), and to illustrate the impacts, we have considered one of the
farm practices that is a heavy user of nitrogen — the continuous corn with
chisel plowing (CC-CH). In the Black Creek area on ridge soils, nitrogen
46
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FIGURE 8: EFFECTS OF FERTILIZATION RATE ON LOW YIELD AND RIVER NITROGEN
.„_ CONCENTRATIONS
120
w.
(J
^ 100
QQ
"5)
> 80
c
V.
O
o
60
40(
—
CORN YIELC
vs
FERTILIZATI
(RIDGE FAR
)
ON
M)
X
/
/
/
*
/
)
/
/
/
'
|/
X
x^
^^*
< —
•i ••
40 80 120 160 200
E
QL
Q.
fc 16
0>
I
o
c
0)
o
o
o
c
0)
OJ
Fertilization-Lbs. Nitrogen per Acre
12
8
±: 4
RIVER NITROGEN
vs
FERTILIZATION
(RIDGE FARM)
NOTE:
ASSUMES 50per cent of EXCESS
NITROGEN IS DENITRIFIED and
0.25m/yr TOTAL FLOW
0
40 80 120 160 200
Fertilization-Lbs. Nitrogen per Acre
47
-------
application is 160 pounds per acre, resulting in corn yields of 130 bushels
per acre. Reduction in nitrogen application of 13 percent is selected and
thus reduces corn yields about 2.5 percent and gross revenue by the same
amount.
TABLE 9: NET REVENUE — 1977 DOLLARS (FERTILIZER TAX* IMPOSED ON NITROGEN)
Farm Practice
Continuous Corn,
Conventional Tillage, with-
out Terracing (CC-CV)
Continuous Corn ,
Conventional Tillage, with
Terracing (CC-CVT)
Continuous Corn,
Chisel Plowing, without
Terracing (CC-CH)
Continuous Corn,
Chisel Plowing, with
Terracing (CC-CHT)
Continuous Corn,
No-Till Planting, with-
out Terracing (CC-NT)
Corn-Soybean ,
Conventional Tillage, with-
out Terracing (CB-CV)
Corn-Soybean ,
Chisel Plowing, without
Terracing (CB-CH)
Corn-Soybean ,
No-Till Planting, without
Terracing (CB-NT)
Corn-Soybean ,
No-Till Planting, with
Terracing (CB-NTT)
Corn-Soybean-Wheat-Hay ,
Conventional Tillage for
Corn only, without Terracing
(CBWH)
Corn-Soybean-Wheat-Hay, No
Till Planting, without
Terracing (CBWH-NT)
*Tax on nitrogen is assumed to
Uplands
$
10,500
7,200
11,100
7,800
4,300
12,400
12,600
11,000
7,400
11,700
12,100
Farm
Rank
7
10
5
8
11
2
1
6
9
4
3
be 7 cents per
Ridge Farm
$ Rank
20,600 5
17,300 10
21,100 4
17,900 9
16,700 11
21,600 3
24,800 1
23,600 2
19,900 7
19,800 8
20,200 6
pound .
Lowlands
$
19,300
16,000
19,900
16,600
3,200
23,100
23,400
15,100
11,500
17,200
16,800
Farm
Rank
4
8
3
7
11
2
1
9
10
5
6
The resulting impact on net revenue is a four percent reduction. If farmers
responded to small changes in fertilizer prices, they would lower their operat-
ing costs by an amount equal to the decline in revenue caused by a fertilizer
tax. In this illustration the 13 percent decrease desired from the use of
nitrogen would be accomplished by a fertilizer tax of about $0.04 to $0.05 per
pound. River nitrogen concentration is reduced by approximately 20 percent
48
-------
(i.e., 18.5 g/m3 to 14.4 g/m3) by the lowered levels of fertilizer use on the
ridge farm. This level of pollutant reduction is explained by Figure 8. It
is seen that the corn-nitrogen response curve is relatively flat in the range
of interest (i.e., large reductions in nitrogen application result in small
reductions in yield). Nevertheless, as the figure shows, the percent reduc-
tion in river nitrogen is greater than the reduction in nitrogen applied to
the crops.
Manure/Legume Subsidies or Restrictions
The final type of policy evaluation considered is a subsidy for construc-
tion of manure storage and handling facilities, or for growing leguminous
cover crops to protect the soil and provide crop nitrogen. Because we have
not included livestock activities in the methodology developed to date, we
consider here only a hay crop subsidy that affects the corn-soybean-wheat-hay
rotations. The objective might be to encourage use of such a rotation to
conserve soil, nitrogen, and energy.
If the lowlands farm is considered, net revenues for maximum net
return — corn-soybean with chisel plowing (CB-CH) — is $24,600. Net reve-
nue for the alternative that we wish to encourage — corn-soybean-wheat-hay
(CBWH) — is $18,00 in the reference case. With a expected yield of four
tons per acre and one-quarter of the farm in hay (62.5 acres), the incremen-
tal price needed to bring the CBWH practice up to the net revenue level for
the CB-CH practice is ($24,600 - $18,100) T (4 x 62.5) = $26 per ton. This
is not impossible, especially if an integrated livestock operation is con-
sidered. However, a subsidy in that amount ($26 per ton or about $100 per
acre) could foster the switch to the CBWH practice at current prices for hay
of $60 per ton.
Alternative Futures
One alternative future is a continuation of the trends toward a highly
concentrated, factory-like food/fiber production system, characterized by
trends listed in Section 1. Aspects of other possible futures evolving out
of past and current trends and new forces might include elements from the
following list.
1) Stabilization of farm sizes and potential reduction in size
of the largest units
2) Reversal of the trend toward absentee ownership
3) Increased labor inputs as energy costs increase
4) Regional and local implement manufacturing operations with
focus on the needs of the part-time small farmer
5) Crop price stabilization through international establishment
of grain reserves
6) Some reversion to polyculture for economic and ecologic
reasons as energy costs increase, to the extent that the
environmental problems of synthetic biocides become a problem
49
-------
7) More use of manure, rotations, and.composted urban organics
for fertilization and biological control for pest management
8) Increasing integration of livestock activities with feed/food
farming as energy costs force more on-farm use of manure as
a feed, fertilizer, and energy (methane) source, and as the
pollution costs of feedlot operations are passed back to the
feedlot operator.
9) State/federal assistance to persons desiring to farm by direct
subsidies (soft loans) and innovative land use controls (e.g.,
purchase of development rights by the state)
10) Adjustments in the organization of marketing and distribution
systems to meet the needs of smaller farm operators
11) Consumer and farmer reaction to costs
In order to carry out evaluations that include these kinds of shifts in
agriculture, a more complete and complex analysis than was possible in this
study is required. However, data exist to explore some of these items and
could be incorporated in an automated farm model.
In this study we can illustrate how a properly structured farm model
would be used to evaluate farm practices and water quality impacts in a
future economic setting. The example concerns increased energy costs, but
does not include changes in labor inputs as suggested in the above list,
item 3.
Many of the inputs to farm production involve the use of energy derived
from oil and natural gas. Farm inputs requiring substantial amounts of
energy include fuels used on the farm and energy that is consumed or embodied
in the production of fertilizers and biocides. For example, in addition to
diesel and gasoline fuels for tractors and combines, corn drying operations
consume about 15,000 Btu per bushel for every ten points of moisture reduc-
tion. Nitrogen fertilizer requires 20,000 to 25,000 Btu for every pound that
is manufactured, while production of biocides requires anywhere from 40,000
to 195,000 Btu per pound depending on their particular formulation.**
Prices paid by farmers for fuels and chemicals will continue to rise
because of diminishing oil and gas reserves and possibly because of the
actions of cartels to create higher oil prices in the long run. It is also
likely that decontrol of natural gas prices will be implemented in the next
five to ten years. It seems reasonable to assume that equal prices will
eventually be established based on Btu content. Farm practices that are
more heavily dependent on mechanization and use of chemicals will be impacted
most severely compared to the less energy-dependent cultivation practices.
We have postulated an economic future for 1985. Prices for tractor and
combine fuel, grain drying operations, and the various chemicals bought by
the farmer will be substantially higher. In the case illustrated here we
^Personal communication, D. Pimental,- Cornell University, November, 17, 1977.
50
-------
assumed 1985 energy prices will be approximately double the 1977 prices,5
while prices for other inputs remain constant. This projected increase is
stated in constant 1977 dollars and therefore does not include inflationary
trends.
Maintaining the same 11 farm practices previously described results in
increased cost of farm operations ranging from $10,000 to $30,000 annually,
depending on the practice. This range corresponds to a 30 tb 65 percent in-
crease over 1977 costs. Net returns are, of course, drastically affected. Need-
less to say, profitability depends on revenues as well as costs. We have
not, however, attempted to project prices received by the farmer for corn,
soybeans, wheat, and hay; even if this had been done, it is possible that
some of the farm practices would no longer appear to be financially viable.
Since we are interested in the potential impacts of farm practices on water
quality as induced by profitability considerations, it is sufficient to
evaluate changes in farm costs without attempting to adjust gross revenues.
A more complex projection would consider substitution, technological change,
and farm scale change effects that are beyond the scope of the present effort.
Table 10 shows the impacts from the future energy prices. On all three
farms the corn-soybean-wheat-hay rotations indicate their lesser dependency
on energy by an upward shift in their net revenue rankings compared to the
reference cases with 1977 energy prices. The impacts are most dramatic on
the uplands farm. The CBWH-NT and CBWH net revenues are ranked one and two
respectively, compared to a 1977 ranking of four and six. Moreover, the
annual soil loss with these two farming practices is four tons per acre or
less, whereas the highest net revenue practice in 1977 (CB-CH) has a soil
loss of 15 tons per acre on the uplands farm.
5Energy Resources, Inc., "Data Resources Outlook for the U.S. Energy Sector:
Control Case," Energy Review. Summer, 1977.
51
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TABLE 10: EFFECT OF FUTURE ENERGY PRICES (CONSTANT 1977 DOLLARS)
Uplands
Farm Practice
Continuous Corn, Conventional
Tillage, without Terracing (CC-CV)
Continuous Corn, Conventional
Tillage, with Terracing (CC-CVT)
Continuous Corn, Chisel Plowing,
without Terracing (CC-CH)
Continuous Corn, Chisel Plowing,
with Terracing (CC-CHT)
Continuous Corn, No-Till Plant-
ing, without Terracing (CC-NT)
Corn-Soybeans , Conventional
Tillage, without Terracing
(CB-CV)
Corn-Soybeans, Chisel Plowing,
without Terracing (CB-CH)
Corn-Soybeans, No-Till Plant-
ing, without Terracing (CB-NT)
Corn-Soybeans, No-Till Plant-
ing, with Terracing (CB-NTT)
Corn-Soybeans-Wheat-Hay ,
Conventional Tillage for Corn
only, without Terracing (CBWH)
Corn-Soybeans-Wheat-Hay ,
No-Till Planting, without
Terracing (CBWH-NT)
1985 net
revenue*
$ rank
- 8,100 8
-11,700 10
- 7,400 7
-11,000 9
-19,900 11
300 4
80 3
- 3,200 5
-7,000 6
+ 50 2
+ 2,600 1
1977
net
rev.
rank
4
9
3
8
11
2
1
7
10
6
4
soil
loss
rank
10
9
7
5
3
11
8
6
4
2
1
Ridge
1985 net
revenue*
$ rank
+ 800 7
- 2,800 9
+ 1,500 6
-2,100 8
- 6,000 11
+11,600 2
+11,800 1
+ 9,300 4
- 5,300 10
+ 8,200 5
+10,700 3
1977
net
rev.
rank
5
10
4
8
11
2
1
3
6
9
7
soil
loss
rank
10
9
7
5
3
11
8
6
4
2
1
Lowlands
1985 net
revenue*
$ rank
- 1,500 7
- 5,000 10
800 6
- 4,400 9
-22,500 11
+ 8,800 2
+ 9,000 1
400 5
- 4,200 8
+ 4,900 4
+ 7,600 3
1977
net
rev.
rank
4
6
3
5
11
2
1
9
10
7
8
soil
loss
rank
10
9
7
5
3
11
8
6
4
2
1
Ul
to
Notes: Highest soil loss rank, 1 = minimum soil loss.
Highest revenue rank, 1 = maximum net revenue.
*Output prices assumed to remain at 1977 level.
-------
SECTION 7
IMPACTS ON DOWNSTREAM USERS
As discussed in Section 6, the results of combining the farm, watershed,
and impoundment models and applying them to a case study area show that the
use of alternative farm practices on different soils has different water
quality impacts. Changes in water quality caused by changing farm practices
have impacts on downstream water users. To estimate these impacts, changes
in water quality must be related to measurements of value to people. If this
could be accomplished, the beneficial impacts of alternative agricultural
practices on downstream users could be compared with the costs (management,
environmental, and social, to farmers and others) of instituting alternative
farming practices. The decision maker could then decide if the beneficial
impacts (benefits) of instituting a particular policy are worth the costs.
This is, however, a difficult step, especially since we are concerned here
with more than one water quality parameter and many downstream users.
A benefit estimation study is a major undertaking in terms of time and
expense and has therefore seldom (or never) been done at the comprehensive
level desirable for estimating the impacts of changes in more than six water
quality variables on a multiple-use impoundment. Table 11 shows alternative
methodologies that are appropriate for measuring different water quality
benefits. Depending on the use of the water and the surrounding land uses,
certain impacts are of more or less interest to groups of people concerned
with water quality. Therefore, it is necessary to determine which groups
are likely to derive the most benefit from which aspects of improved water
quality.
As an example of the interests of different groups, let us assume that
the watershed in which the farmland is located drains into a small stream
used by local sport fishermen in the spring. Downstream is an impoundment
created for the purposes of water supply, flood control, and recreation.
The impoundment is a major recreational and aesthetic attraction in the
region, attracting people from surrounding counties to swim, boat, fish, and
picnic. Let us also assume that a town uses the reservoir to supply water
for drinking and other purposes. Some benefit categories of interest in this
case are: human health, municipal water supply, flood control, ecology,
recreation, aesthetics, and the local economy. The methods of benefit esti-
mation vary according to the benefit categories of interest and have been
discussed and evaluated according to the criteria outlined in Appendix E.
The following paragraphs briefly indicate possible research approaches for
each of the above categories.
53
-------
TABLE 11: COMPARISON OF METHODOLOGIES TO MEASURE WATER QUALITY BENEFITS
time
budget
bidding
games
travel
costs
marginal
costs
net factor
income
market
study
non-dollar
measurement
input/out-
put model
alternative
U)
aesthetic
X
ranking
c
recreatio
X
X
ranking
property
values
X
Benefit Categories
I!
medical
costs
ft lost
earnings
-H
commercia
fishing
yield
change
x price
.
municipal
water supp
treatment
sroduction
costs
H ^
industria
water sapp]
treatment
production
costs
•-t --.
«J H
dredging
(navigatior
flood contrc
X
o
•H
o
a
change
in
habitat
cost to
reproduce
local or
regional
economy
X
Human Health. Epidemiological data must be gathered and analyzed to
relate morbidity and mortality rates to drinking water nitrate or biocide
levels or both. Health effects would then be related to their value to
people either by: 1) calculating a dollar value for medical costs and lost
earnings for each rate of morbidity and mortality; 2) surveying the relevant
population using a bidding game approach to determine aggregate willingness-
to-pay to avoid each level of health effect; or 3) a combination of both of
these methodologies.
Municipal Water Supply. Variations in treatment cost, including equip-
ment and maintenance costs, must be estimated for alternative pollutant
(sediment, etc.) levels.
Flood Control. Sediment deposition affects frequency and severity of
flooding. This relationship also must be specified, and the cost of related
flood damage calculated.
Ecology. One possible approach ranks habitat changes that affect growth
of organisms caused by water quality changes. Diversity is one criterion
used to define this ranking. Another approach would be to calculate the
cost of reproducing the function that the ecology of the region provides and
that would be altered by water quality changes.
54
-------
Recreation. Recreation covers both contact activities such as swimming,
and non-contact activities such as boating. The travel-cost method is one
of the accepted methodologies available to construct a demand function
dependent on alternative levels of water quality, using data on variations
in distance traveled to recreation sites as a surrogate price for the acti-
vity. This method may not be the best choice, since in one example most of
the users of this impoundment are local and do not travel long distances.
Another approach, the bidding game, relies on survey data to indicate
the highest amount people would be willing to pay for an improvement in
water quality. The bidding can be tied into an appropriate mechanism such
as a water bill, a recreation fee, or a tax. Results, however, seem depen-
dent on assumed starting bids.
In the time budget approach, also using a survey format, respondents
describe their activities and expenses during a certain time period — a
week, for example — which are then matched with certain levels of environ-
mental quality. This information is used to build a demand curve.
For sport fishing, another important recreational activity, benefits
accruing to fishing have been related to a fish response model. This model
simulates fish responses in terms of quantity and type to water quality
changes. With commercial fishing, benefits could be derived by translating
the particular fish population into a dollar measure of changes in income,
assuming constant prices. Sport fishing variables other than success are
important to the recreational experience. In might be possible to combine
the fish response device with one of the survey methods described above to
obtain information on sport fishing benefits.
Aesthetics. The aesthetic and visual aspects of the river or impound-
ment water quality are determined by attributes such as color, depth percep-
tion, the existence of weeds, etc.
One approach would be to consider aesthetics along with recreation bene-
fits in a time-budget or bidding game survey. The population sample sur-
veyed would then be expanded to include non-recreationists. Typically, rank-
ing methods have been used to ascertain the value of the aesthetic qualities
of natural resources. One difficulty is that the aesthetic value of a water
body is greatly influenced by its surroundings and characteristics other
than water quality. A good non-monetary ranking system used in conjunction
with the survey methods would be valuable as a reliability check.
Local Economy. An input/output model could be constructed for the
regional economy surrounding the impacted water body. Increased expenditures
generated by recreationists or tourists (see above) in response to changes
in water quality could be used in the model to calculate the resulting in-
crease in household income and local production.
We have outlined possible elements of a comprehensive benefit estima-
tion methodology. It is clear that such a study would require significant
time and resources to implement and would present many empirical difficul-
ties. As an alternative, we would like to present a simplified version that
55
-------
qualitatively assesses the direction of benefits resulting from water quality
changes induced by the alternative farming practices. This is considered a
substitute for the major effort which would be required to implement a quan-
titative benefit estimation methodology.
Table 12 indicates which water quality components impact which benefit
categories. A minus sign indicates that an increase in the water quality
measurement has a detrimental effect on the specified benefit group; for
example, an increase in nitrogen concentration in drinking water is poten-
tially harmful to human health. A zero indicates that an increase in the
parameter is of no importance to the benefit category. For instance, the
same increase in nitrogen concentration just mentioned would not impact dredg-
ing operations in the impoundment. A water quality measurement increase which
has a positive impact on a benefit category is indicated by a plus sign.
Increasing impoundment biomass, for example, might improve sport fishing,
since more food might increase the available fish population.
TABLE 12: IMPACTS ON BENEFIT CATEGORIES OF WATER QUALITY COMPONENTS*
Benefit
Categories**
human health
(drinking water)
municipal
water supply
flood control
ecology
recreation
sport fishing
contact
non-contact
aesthetics
local economy
Water Quality Components
Impoundment
Sedimentation
(kg/m2)
0
-
-
-
-
o(-)
o(-)
o(-)
-
Impoundment
Sediment
outflow
Concentration
(kg/m3)
-
-M
0
-
-
-
-
-
-
River and
Impoundment
Nitrogen
(g/m3)
-
-
0
-
0
-
0
0
-
River Light
Extinction
Coefficient
(m'1)
-
-
0
-
-
-
-
-
-
Impoundment
Light
Extinction
Coefficient
-
-
0
-
-
-
-
-
-
Impoundment
Biomass
(g chloro-
phyll-a/m3)
-
-
0
-
+(-)
-
-
-
-W
•The effect on a benefit category of an increase in any parameter is noted as follows:
detriment - -; no effe.t » 0; benefit - +.
"See text for explanation of benefit categories.
There are several cases in which the impact of a water quality change
on a benefit category is not totally clear. These are noted by alternative
signs in parentheses. Four such cases are evident in Table 12:
56
-------
1) Sedimentation in a municipal water supply is mainly detrimental
because it causes turbidity, carries chemicals and other toxic
materials, and, if it occurs in high concentrations, must be
removed during treatment. On the other hand, sediment does
tend to adsorb odor and taste-producing chemicals which might
otherwise require artificial flocculation (coagulation). This
possible benefit is considered to be less important than the
detriment, and therefore a minus sign is used to show the
dominant effect.
2) An increase in impoundment biomass may have a positive effect
on sport fishing, since it means an increase in food supply
for fish and hence in fishing success. With excessive amounts
of algal growth, however, bottom conditions deteriorate and
dissolved oxygen levels decrease, causing a decrease in desir-
able fish species, such as trout, and an increase in trash fish,
which survive better under such conditions. This may ultimately
have a negative impact on sport fishing. In our case example,
however, we assume that increasing biomass levels can be viewed
as beneficial to sport fishing.
3) The local economy benefit category is dependent on the benefits
to tourists and recreationists, and therefore the water quality
impacts observed will be positive or negative according to the
impacts on the recreation and aesthetic benefit categories.
Since an increase in biomass has a negative impact on contact
and non-contact recreation as well as aesthetics, it will most
probably have a negative impact on the local economy despite
its generally positive impact on sport fishing. The opposite
would be true only if much of the local economy were dependent
on an influx of fishermen, which we did not assume.
4) Sedimentation reduces the holding capacity of an impoundment.
When this effect is slight and the impoundment is large, there
will be insignificant impacts on contact and non-contact recrea-
tion and aesthetics — assumed in Table 12. However, in some
cases sedimentation could be a very grave problem in an impound-
ment, causing it to fill in and cease to exist.
It is clear from Table 12 that with the possible exception of the beneficial
impact of higher biomass levels on sport fishing, all categories are either
not influenced or negatively influenced by an increase in any of the water
quality components.
In order to compare the practices from the downstream users' point of
view we need to select a base case; this is the case option producing the
highest net revenue (the corn-soybean rotation using chisel plowing), essen-
tially assuming that the farmer is a maximizer of net revenue. Figures 9,
10, and 11 depict the relative water quality and net revenue impacts (measured
as percentage increases or decreases relative to the base case) of the other
ten practices on the various soil types.
57
-------
oo
Fir.tlRF 9: PKRCEHT CHANGE OF H1W1KST REVENUE FACTOR — I.OWLAND
REVENUE
SOL LOSS
40
0
-40
-60
SEDIMENTATION
40
0
-40
-80
RIVER NITROGEN
(%) 120
80
40
0
-20
RIVER PHOSPHORUS
(%) 20r
1
fy/ff^f*
RIVER LIGHT EXTINCTION COEFFICIENT
(%) 60
20-
0
-20
-SO-
IMPOUNDMENT LIGHT EXTINCTION COEFFICIENT
(%) 20r
°F
-201
IMPOUNDMENT BIOMASS
(%) 20
J L_J
FIGURE 10: PERCENT CHANGE OF HIGHEST REVPNIIE FACTOR — RItXiF.
REVENUE (%)
SOIL LOSS CM
7/7,
80
40
0
-4O
-80
40
SEDIMENTATION (%) 0
-40
-80
80
4O
RIVER NITROGEN (%) 0
-40
RIVER PHOSPHORUS 0 EESfaJ^zra
(%) -20
40 :
RIVER LIGHT
EXTINCTION COEFFICIENT 0
(%) -40 L
IMPOUNDMENT LIGHT 20
EXTINCTION COEFFICIENT 0
IMPOUNDMENT BIOMASS
X%)
II
-------
FIGURE 11. PERCENT CHANGE OF HIGH-
EST REVENUE FACTOR—UPLANDS
REVENUE CM
SOIL LOSS (%)
SEDIMENTATION (%)
RIVER NITROGEN OW
RIVER
PHOSPHORUS
nu
The downstream benefits of alternative
farming practices can be qualitatively
compared by mapping the quantitative
practices and water quality relation-
ships depicted on Figures 9, 10, and
11 onto the qualitative water quality
benefit relationships presented in
Table 12. Results are summarized in
Table 13 for a comparison of the
corn-bean-wheat-hay rotation with the
assumed base case (corn-soybean rota-
tion with chisel tillage). The rows
in Table 13 correspond to different
benefit categories, and the columns
to different water quality components.
As in Table 12, a positive sign indi-
cates that switching from the base
case to the compared practice pro-
duced a beneficial impact on the
corresponding benefit category. The
percentage changes in the various
water quality components, necessarily
considered in evaluating the results,
are also listed in Table 13. The
only negative impact of switching to
the CBWH rotation is related to the
impoundment biomass column — namely,
the impact on sport fishing; however,
the mere three percent change in bio-
mass indicates that this negative
impact might be minor relative to the
positive impacts on sport fishing
operating through the other water
quality components. The most pronounced beneficial impacts are due to reduc-
tions in impoundment sedimentation, impoundment suspended solids concentra-
tions, and river extinction coefficients.
In order to develop an aggregate estimate of the impact of any practice
on any given benefit category, the relationships between the levels of the
various water quality components and the degree of benefit derived by each
user would have to be defined.
This could be done possibly using an approach similar to that taken by
Meta Systems in assessing the impact of each alternative canal route of the
proposed Cross Florida Barge Canal on all the habitats of the canal zone —
as perceived by each interest group. 1 However, data constraints do not permit
these estimates , at least at this stage of the methodology development.
RIVER LIGHT
EXTINCTION COEFFICIENT OiJ
IMPOUNDMENT LIGHT
EXTINCTION COEFFICIENT 0
<%) -40
IMPOUNDMENT BIOMASS
1 Meta Systems Inc, The Overall Assessment for the Cross Florida Barge Canal
Project. Contract No. DACW 17-75-C-0077, U.S. Army Corps of Engineers, Jack-
sonville District, Cambridge, Massachusetts, May, 1976.
59
-------
TABLE 13. RELATIVE IMPACTS OF CBWH PRACTICE ON HATER QUALITY COMPONENTS AND
BENEFIT CATEGORIES FOR THE LOWLAND SOIL TYPE •*
Percent Increase
Iron Base Case(CBWH)*
Benefit categories *"
human health
(drinking water)
municipal
water supply
dredging
(flood control)
ecology
recreation
sport fishing
contact
non-contact
aesthetics
local economy
Water Quality Components
Impoundment
Sedimentation
-70»
B E
0
+
+
+
•f
0
0
o
+
Impoundment
Sediment
Outflow
Concentration
-691
N E F
+
•fr
0
+
•f
+
+
+
+
River and
Impoundment
Nitrogen
-20%
I T
•f
+
0
+
0
+
0
0
•»•
River Light
Extinction
Coefficient
-59*
IMP
+
•f
0
•f
+
+
+
+
+
Impoundment
Light
Extinction
Coefficient
-18»
Impoundment
Biomass
-3\
ACTS
•f
+
0
+
•i
•f
+
+
•f
+
+
0
+
-
•f
+
+
+
* The base case is the highest revenue producing alternative (CB-CH). The effect on a benefit category of
an increase in any parameter compared to the base case is noted as follows: detriment - -j no effect • Oj
benefit » +. A decrease would have the opposite sign (See Table 11).
•* See farm model discussion for definition of fanning practices.
*** See text for explanation of benefit categories.
If an aggregate measure can be derived within each benefit category, the
next level of analysis is the traditional benefit analysis striving for one
scalor to the extent feasible. This number would be the sum of the water
quality impacts (as weighted by each group) aggregated across all the groups.
As discussed in the analysis of the Cross Florida Barge Canal and other pro-
jects, the major difficulty, perhaps the ultimate reason for the inapplic-
ability of the approach at the local/regional level, is the selection of the
various weighting factors (based on political, social, and economic aspects)
permitting the necessary aggregation. Furthermore, the fact that different
groups follow their interests implies that computing an overall scalor might
not be helpful in evaluating alternative agricultural practices and their
various impacts.
If an aggregate measure of benefits to downstream users could be defined,
comparison with the aggregated costs incurred by upstream farmers would lead
to a measure of net benefits. However, considering the fact that upstream
users incur different costs dependent upon pertinent policies, locations,
soil, etc., the aggregate upstream cost does not reflect realities of conflict
among farmers. These questions have not yet been adequately addressed within
the overall framework.
60
-------
Rather than attempting to account for all the considerations just men-
tioned, we have completed in Table 14 a simple summary of the relative impacts
of 11 farm practices (each developed in a table similar to Table 13) on the
benefit categories of interest. No attempt has been made here to weigh water
quality components or benefit categories. We feel that while there are cer-
tain gains to be made in pursuing the traditional approach, it may be most
worthwhile in the short run to examine possible non-monetary approaches that
allow for various weighting schemes to compare upstream and downstream bene-
fits and de-benefits associated with various uses (users).
TABLE 14: SUMMARY OF RELATIVE IMPACTS OF FARMING PRACTICES ON BENEFIT
CATEGORIES
Soil Type: Lowlands Farming Practices*
Benefit
Categories**
human health
(drinking
water)
municipal
water sup-
ply
dredging
(flood con-
trol)
ecology
recreation
sport fish-
ing
contact
non-contact
aesthetics
local economy
CC-CV
K+) +
1(0)
4(-)
K+).
5(-)
5(0)
K-)
K+)
5(-)
1(0)
5(-)
K-)
1(0)
4(->
K+)
2(0)
3(-)
K+)
2(0)
3(-)
K+)
5(-)
CC-CH
2( + )
1(0)
3(-)
3(+)
3(-)
K+)
5(0)
3(+)
3(-)
4(+)
1(0)
K-)
2(+)
1(0)
3(-)
2(+)
2(0)
2(-)
2(+)
2(0)
2(-)
3(+)
3(-)
CC-NT
2( + )
1(0)
3(-)
3(+)
3(-)
K+)
5(0)
3(+)
3(-)
4(+)
1(0)
l(-)
2(+)
1(0)
3(-)
2(+)
2(0)
2(-)
2(+)
2(0)
2(-)
3(+)
3(-)
CB-CV
K + )
2(0)
3(-)
K+)
1(0)
4(-)
5(0)
K-)
K+)
1(0)
4(-)
1(0)
5(-)
K+)
2(0)
3(-)
K + )
2(0)
3(-)
K+)
2(0)
3(-)
K+)
1(0)
4(-)
CB-CH
6(0)
6(0)
6(0)
6(0)
6(0)
6(0)
6(0)
6(0)
6(0)
CB-NT
2(+)
KO)
3(-)
3(+)
3(-)
K+)
5(0)
3(+)
3(-)
4(+)
1(0)
l(-)
2(+)
1(0)
3(-)
2(+)
2(0)
2(~)
2(+)
2(0)
2(-)
3(+)
3(-)
CBWH
5( + )
KO)
6(+)
K+)
5(0)
6(+)
4( + )
1(0)
l(-)
5(+)
1(0)
4(+)
2(0)
4( + )
2(0)
6(+)
CBWH-NT
4( + )
2(0)
5( + )
1(0)
K+)
5(0)
5( + )
KO)
4(+)
2(0)
4(+)
2(0)
3(+)
3(0)
3(+)
3(0)
5(+)
1(0)
CC-CVT
K+)
2(0)
3(-)
K + )
1(0)
4(-)
5(0)
K-)
K+)
1(0)
4(-)
2(0)
4(-)
K+)
2(0)
3(-)
H+)
3(0)
2(-)
K+)
3(0)
2(-)
K+)
1(0)
4(-)
CC-CHT
2(+)
2(0)
3(-)'
3(+)
1(0)
K-)
K+)
5(0)
3(+)
1(0)
2(-)
4(+)
2(0)
2(+)
2(0)
2(-)
2(+)
3(0)
K-)
2(+)
3(0)
l(-)
3(+)
KO)
2(-)
CB-NTT
2(+)
1(0)
2(-)
3(+)
3(-)
K+)
5(0)
3(+)
3(-)
4(+)
1(0)
l(-)
2(+)
1(0)
3(-)
2( + )
2(0)
2(-)
2(+)
2(0)
2(-)
3(+)
3(-)
*See farm model discussion for definition of farming practices.
** See text for explanation of benefit categories.
+Sum of the effects on a genefit category of a change from the base case (CB-
CH) to another farming practice. Six water quality components are evaluated.
Numbers indicate the number of water quality component changes that have a
positive, negative, or no effect on the benefit category. Detriment = -;
no effect = 0; benefit = +.
61
-------
REFERENCES
Section 1
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-------
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Section 7
Meta Systems Inc, The Overall Assessment for the Cross Florida Barge Canal
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64
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Appendix A
Farm Model
Introduction
The development of the farm budget is presented in this appendix.
The model assumes that the farmer is a profit maximizer and will choose
the farming practice which gives him the highest net revenue. The pur-
pose of the budget approach is to show the effects on net farm revenue
of different farming practices considered because of their potential
for reducing nonpoint source pollution for agriculture. This model is
based on a farm budget developed by Dr. Klaus Alt of Iowa State Univer-
sity, Ames, Iowa and discussed in Appendix C "Economic Analysis Method-
ology" of USDA and U.S. EPA, Control of Water Pollution from Cropland,
Vol. II.
The farm budgets shown here are based on eleven farming practices
which are appropriate for use on farms in the Black Creek area of north-
eastern Indiana near Fort Wayne. The most commonly used cropping practices
in the case study are are included, corn and corn-soybean rotation, and
the most common method of cultivation, conventional tillage, which includes
fall plowing with a moldboard plow. In addition to these practices, two
reduced tillage practices, chisel tillage and a no-till option, are applied
to these two cropping patterns to examine their effects on net revenue and
water quality. Chisel tillage involves shredding stalks and chisel plowing
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in the fall and disking in the spring. The no-tillage option is defined
as shredding stalks in the fall and planting in the spring using a no-till
planter. A more extensive crop rotation of corn-soybean-wheat-meadow,
involving field cover crops as well as row crops, is also examined.
This rotation is considered with two tillage options, one in which the
meadow is plowed in the fall using a moldboard plow before planting the
corn in the spring and the other in which herbicides are used in the
spring to kill the remaining sod before planting corn with a no-till
planter. Terracing, a structural erosion control measure, was added
to three of the above eight practices, continuous corn, both convention-
ally tilled and chisel tilled, and a no-till corn-soybean rotation.
Farm budgets were developed for three typical farms of two hundred
and fifty acres each located on three soil types. The soil types,
upland, ridge and lowland, were selected as representative of soils in
the case study region. The uplands can be characterized as a Blount-
Morley-Pewamo association, the ridge as a Rensselaer-Whitaker-Oshtemo
association and the lowlands as a Hoytville-Nappanee association.
Some of the farming practice costs vary depending on which soil type
the farm is located.
Tables A-l through A-10 show detailed costs for the inputs, ranging
from equipment to seeds, required for using each of the eleven practices
on each of the farms. Table A-ll shows expected yield and gross revenue
for each practice and Table A-12 presents a summary of all the costs as
well as gross and net revenue for each practice on each soil type. The
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practices are ranked in terms of net revenue in Table A-12 and in terms
of soil loss in Table A-13.
Following this presentation of the basic farm budget model for
the eleven practices considered is the development of six alternative
situations and policies. The use of the model here is to show how these
alternatives impact net farm revenue and in turn affect the choice of
the farmer. Ultimately the implementation of any agricultural policy
will rest on the decisions made by the individual farmer.
The assumption is made in Alternative A that the farmer hires
custom operators to carry out certain tasks in the two extensive crop
rotation practices considered. This results in increased net return for
these two practices. The net revenues developed for these two
practices in this alternative are used in Section 6 of the main
report as part of the base case. Custom hiring was not assumed in
Alternatives B through F, following, which are preliminary.
Alternative B represents a future scenario in which energy prices
more than double compared to other prices. This case was developed to
illustrate how the farm model can be used to examine the robustness of
agricultural policies under alternative futures.
The last four alternatives, C, D, E and F illustrate the effects
of agricultural policies which might be implemented to encourage farmers
to adopt practices which are beneficial to water quality or which are
aimed directly at controlling farm factor inputs which are detrimental
to water quality.
79
-------
Table A-l. Terraces
Terrace costs were calculated on the basis of cost per linear
foot of terrace as experienced in the Black Creek Project. This
includes the cost of associated tile drains. Since the slope
length is relatively short compared to the terrace spacing so that
there is one terrace per slope, as we assumed here, then the approx-
imate number of feet of terrace per acre is calculated by dividing
43,560 (the number of square feet per acre) by the terrace spacing.
This is the method suggested in the Midwest Farm Planning Manual
(Third edition, ISU Press, Ames, Iowa, 1973, revised 1975).
While not the case in our study, if more than one terrace per
acre is specified, as in Table A-l, Appendix C, Control of Water
Pollution from Cropland, then the number of feet of terrace per
acre is estimated by dividing 43,560 by the slope length and mul-
tiplying by the number of terraces per slope. Other items were
calculated as indicated in the footnotes.
It was assumed for simplification purposes that every acre was
terraced. It should be noted that the values used for terrace spacing,
slope length and cost per foot of terrace were generalizations applied
to the whole watershed area, and would vary considerably from farm to
farm in actual practice.
80
-------
Table A-l
Terrace Costs*
Item
Amount
Terrace spacing, feet** 180
Slope length, feet* 300
Number of terraces per slope* 1
Feet of terrace per acre 242
Construction cost per foot terrace ($)*** 1.00
Construction cost per acre {$) 242
Prorated construction cost ($) 25.81
Maintenance cost, foot ($) 0.00011
Maintenance cost, acre ($) 0.03
Yearly terrace charge per acre ($) 25.84
Total yearly terrace charge (250 acres) ($) 6,460.00
* Assume slope length 300 feet and one terrace per slope.
** Daniel McCain, District Conservationist, Allen County Soil
Conservation District, estimate for Black Creek Watershed.
*** James Lake, Black Creek Project Administrator, estimate for Black
Creek Watershed ($1.00-$1.25). Joseph Pedon, Agronomist, Indiana
Soil Conservation Service, Indianappolis, recommended use of lower figure
to account for increased contractor experience over time.
+ Assume 15 year life (from Daniel McCain, District Conservationist,
Allen County Soil Conservation District) and interest at 8 percent.
Average yearly interest = [(initial cost + salvage value)/2] x i rate.
Prorated construction cost = average yearly interest + [(initial cost)/
(economic life)]. Assume salvage value = 0.
++ Assumed one-half of maintenance cost used in Sidney James (ed.),
Midwest Farm Planning Manual, Third edition, ISU Press, Ames, Iowa,
1973, revised 1975, p. 34, after discussion with Joseph Pedon, Agronomist,
Indiana Soil Conservation Service.
81
-------
Table A-2. Machinery Fixed Costs
Specifications for the farm equipment for each farming
practice were developed using the equipment listed in Table 2,
Appendix C, Control of Water Pollution from Cropland as a base,
with modifications appropriate for current farming practices in
northeastern Indiana. Discussions with local equipment dealers
and with Dr. Howard Doster, Dr. Harry Galloway and Dr. Donald
Griffith at Purdue University provided information for making
the modifications.
There are many variations available to the farmer for each
item listed in Table A-2. Here, an attempt was made to insure that
the equipment specified was appropriate for the soil conditions,
reflected current farming practices for a well managed farm,
including recent technology changes and was appropriately sized
so that, for example, the plow was not oversized compared to the
tractor.
Current list prices for the farm machines were calculated,
for the most part, by averaging local equipment dealers estimates.
As a check, current Ames, Iowa prices were also obtained as well
as a national USDA price index which was used to update the prices
in Appendix C, Control of Water Pollution from Cropland.
Other items in Table A-2 were calculated as indicated in the
footnotes using data from Appendix C, Control of Water Pollution
from Cropland, and from the Purdue Crop Budget.
82
-------
Table A-2. Machinery Fixed Costs
CD
Yearly
Machine
Stalk Shredder
Moldboard Plow
Chisel Plow
Disk
Harrow
Sprayer
Planter
No-till Planter
wheat Drill
Cultivator
Combine
Platform
Corn Head
Hay Mower/
Conditioner
Hay Rake
Hay Baler
Initial List
Size & Other Specs. Price ($J*
12' flail
5-16"; high clearance;
sheer bolt
10'; three bar; straight
shank; pull type
20'; tandem; hydraulic
20'; hydraulic mounted
tractor mounted (rear);
120" boom size
4-30"; conventional;
no fertilizer attachments
4-30"; fluted coulters;
no fertilizer attachments
12'; with grass seeding
attachments
4-30"; rear mount
Small (70-80 hp) ; self-
propelled; diesel
13'; hydraulic; with
cutter bar
4-30"; picker-sheller
7'
Side delivery
PTO; 50-60 Ib bales;
rectangle bales; twine
3,050
4,000
2,150
6,750
750
1,400
4,000
5,500
4,250
2,000
27,100
3,850
7,800
4,800
1,250
5,100
Salvage**
Value (%)
13
17
13
17
17
17
17
17
9
17
18
18
18
12
12
21
.7
.7
.7
.7
.7
.7
.7
.7
.7
.7
.9
.9
.9
.5
.5
.1
Depreciation
Economic** (straight line
Life method)
12
10
12
10
12
10
10
10
14
10
10
10
10
12
12
8
219.
329.
154.
555.
51.
115.
329.
452.
274.
164.
2,197.
312.
632.
350.
91.
502.
35
20
62
53
44
22
20
65
13
60
81
24
58
00
15
99
Yearly
Taxes, Insurance*** Fixed
and Housing Interest Cost
137.
180.
96.
303.
33.
63.
180.
247.
191.
90.
1,219.
173.
351.
216.
56.
229.
25
00
75
75
75
00
00
50
25
00
50
25
00
00
25
50
138.71
188.32
97.78
317.79
35.31
65.91
188.32
258.94
186.49
94.16
1,288.88
183.11
370.97
216.00
56.25
247.04
495.31
697.52
349.15
1,177.07
120.50
244.13
697.52
959.09
651.87
348.76
4,706.19
668.60
1,354.55
782.00
203.65
979.53
* Prices are averages of local Indiana equipment dealer 1977 estimates except for no-till planter price which is from the Department
of Agricultural Economics, Iowa State University for 1977.
** Table 2 Appendix C, Control of Water Pollution from Cropland, Vol. II, U.S. Government Printing Office, Washington, D.C. , 1976.
*** Taxes two percent, insurance one and a half percent of initial cost, Purdue Crop Budget, p. 22; housing one percent. Appendix C, Table 2.
+ Eight percent per year, Purdue Crop Budget, Department of Agricultural Economics, Purdue University, Lafayette, Indiana, 1977, p. 22.
t(I+S)/2J r = yearly cost.
-------
Table A-3. Machinery Costs
Data from Table 3, Appendix C/ Control of Water Pollution
from Cropland, Vol. II, were used as the basis for this table.
The eight farm practices considered were developed from those
listed in Appendix C, Control of Water Pollution from Cropland,
Vol. II, with modifications so that they represented some of the
tillage practices and crop rotations used in the tillage trials
in the EPA Black Creek demonstration project. Dr. Daniel McCain,
Allen County Soil Conservation District, and Mr. James Morrison
and Dr. Donald Griffith of Purdue University provided guidance for
the selection of the practices described in Table A-3.
The practices were chosen to reflect the effects of changes
in tillage methods and changes in rotation of crops. Continuous
corn and a corn-soybean rotation are each subjected to three
farming practices, conventional tillage, reduced tillage, and no
tillage. A more extensive rotation consisting of corn, soybean,
wheat, meadow is also included, subject to two tillage practices,
one in which the meadow is plowed conventionally before the corn
is planted and the other in which the meadow is treated with herbi-
cide and the corn planted directly in the remaining sod.
Tables A-4 through A-12 show eleven farming practices. These
include the eight from Table A-3 plus three from Table A-3 with ter-
racing added: continuous corn, conventional tillage; continuous
corn, chisel tillage; corn-bean rotation, no-till planting.
84
-------
Hours per acre data were taken from Appendix C, Control of
Water Pollution from Cropland, Vol. II, and reviewed with Black
Creek project personnel. Equipment specification changes made
some updated figures necessary; sources for updated figures are
noted on the table. Most implements are used only once over the
field except for disking for chisel plow and hay harvesting equip-
ment. For these implements the times over is variable and the
number shown is the average. Total hours is equal to the product
of hours/acre, acres of use and times over. Repair costs per
100 hours for the harrow were calculated from Appendix C (Control
of Water Pollution from Cropland, Vol II) data to be three percent
and for the hay mower/conditioner, seven percent.
85
-------
Table A-3. Machinery Costs
CD
lapleaent
Hours/
Acre3
Acresfc Times Total
of use Overc hours
Repair
Cost/
100 hrs, S
Total
Yearly
Fixed Total
Corn, fall turn-plow, conventional
•oldboard plow6
harrow ~ "
sprayer
planter
cultivator
coabine ~~
Corn, fall shred stalks, chisel plow, spring disk
.36
.10
.10
.21
.21
.479
.479
250
250
250 ]
250 ]
250 ]
250 ]
250 ]
250 :
L 90
L 25
L 25
L 52.50
42.50
52.50
L 117. 5O
117.50
200
337
70
320
100
542
156
00
50
50
00
00
00
00
00
180
84_
36
136
52
636
183
00
38
63
75
00
50
85
30
697.52 877
1177.07 1261
120.50 126
244.13 280
697.52 815
346.76 401
4706.19 5343
1354.55 1537
10,643
45
13
JH)
04
85
stain
harroi
spray<
plant.
corn 1
Total
snreaaer 18
sr ~ ~" 21
»? TT79
lead 47g
250
250 ]
250
250 ]
250 ]
250
250 ]
L 45
L 52.
L.5 37
L.5 37
L 42
L 117
L 117
.50
50
50
.50
50
50
122
337
70
320
542
156
00
50
50
50
00
00
00
00
54
56
126
8
36
136
636
183
.90
.44
.56
.44
75
00
85
30
495
349
1177
120
244
697
4706
1354
31
15
07
50
_U
J.9
550
405
1303
128
280
815
5343
1537
10,365
21
59
63
94
88
04
85
66
stalk shredder
sprayer "
no-till planter
Notes (see following pages)
.18
.21
22
.479
250
250
250
250
1 45 122.00
1 52.50 70.00
1 55 440.00
1 117.50 542.00
1 117.50 156.00
242.00 959.09 1201.09
183.30 1354.55 1537.85
-------
Table A-3 (continued)
oo
Implement
Corn-soybeans, fall turn-plow, conventional
aoldboard plow
disk
harrow
sprayer
planter
cultivator
combine corn
coaLine soybeans
corn head
platform
Total
Corn-soybeans, fall shred, chisel plow, spring disk
stalk shredder
chisel plow
disk
harrow
sprayer
planter
combine corn
combine soybeans
corn head
platform
Total
Corn- soybeans, fall shred, no-till plant
stalk shredder
no-till planter
Hours/
Acre*
.36
.10
.10
.21
.17 'f
.21
.479
.30
.479
.30
.18
.2lh
.10
.10
.21
.17f
.479
.30
.479
.30
.18
Acres
of use15
250
250
250
250
250
250
125
125
125
125
125
250
250
250
250
250
125
125
125
125
125
250
Times
Overc
1
1
1
1
1
1
1
1
1
1
1
.5
.5
1
1
Total
hours
90
25
25
52.50
42.50
52.50
96 25
58.75
37.50
22.50
52.50
37.50
37.50
52.50
42.50
96.25
58.75
37.50
22.50
.55.00
Repair
Cost/
100 his,
200.00
337.50
22.50
70.00
320.00
100.00
542 00
156.00
77.00
122.00
107 . 50
337.50
22.50
70.00
320.00
542.00
156.00
77.00
122.00
440.00
Total
Repair
5d Cost, $
180.00
84.38
5.63
36.75
136.00
52.50
521 68
91.65
28.88
27.45
56.44
126.56
8.44
36.75
136.00
521.68
91.65
28.88
27.45
36 75
242.00
Yearly
Fixed
Cost, $
697.52
1177.07
120.50
244.13
697 . 52
348.76
4706 19
1354.55
668.60
495.31
349.15
1177.07
120.50
244.13
697 . 52
4706.19
1354.55
668.60
495.31
244 13
959.09
Total
Cost, S
877.52
1261.45
126.13
280.88
815.52
401.26
5227 87
1446.20
697 . 48
11,134.31
522.76
405.59
1303.63
128.94
280.88
815.52
5227.87
1446.20
697 . 48
10,828.87
522.76
280.88
1201.09
Notes (see following pages)
-------
Table A-3 (continued)
oo
00
Implement
Corn-soybeans, fall shred, no-till plant (continued)
combine corn
combine soybeans
platform ~~ ~ ' "
Hours/
Acre3
.479
.30
.479
Corn-soybeans-wheat-meadow, fall turn-plow corn, fall shred, no-till
•talk shredder
•oldboard plow
•prayer -
no-till planter
wheat drill ~~
combine corn
combine wheat
corn head
platform ~
hay mower/conditioner
hay rake
hay balei
Corn-soybeans-wheat-meadow, fall shred, no-till plant
•talk shredder
harrow ~ " ~ ' ~~
•prayer
no-till planter
wheat drill " ~
Motes (see following page)
.18
.36
.10
.10
.21
.22
.25
.479
.30
.479
.30
Ho
.63
.18
.10
.10
.21
.25
Acres Times Total
of useb Overc hours
125
125
125
plant
62
62,
125
125
125
125
62
62
62,
62
125
62
62
62
62
62
62
125
62.
1
- j 96
1 58
others
.5
5
5
5
.5 ]
_5
5
5
5
l±
12.
26_
27
15_
__
66
29
37
.5 74
.5 65
.5 137
11
6.
6.
1 26.
27.
5 15.
.25
ITS
.50
.50
.50
.50
63
88
38
50
38
63
81
50
63
Repair Total
Cost/ Repair
1OO hrs, Sd Cost, $
542.00
156. OO
122.OO
200.00
337.50
22.50
70.00
440.00
340.00
542.00
156.00
77.00
336.00
306.00
122.00
337.50
22.50
70.00
440.00
340.00
521
91
28
13
45
42
18
121
52
362
45
28
249
49
421
13
21
1
18
121
52
.68
.65
.73
.00
.19
.81
.38
.00
.22
.49
.83
.88
.22
73
09
41
38
00
Yearly
Fixed
Cost, $
4706
1354
495
697
1177
120
244
959
651
4706
1354
668
782
979
495
1177
120
244
959
651
.19
.55
.07
.13
.09
.87
.19
.60
i
31
07
13
09
87
Total
Cost, $
5227.87
1446.20
262.51
1080.09
704.09
1400.38
__697_.48_
1198.16
121.91
262.51
1080.09
704.09
-------
Table A-3 (continued)
CO
Hours/
Implement Acre
Acres
of useb
Times Total
Overc hours
Repair Total
Cost/ Repair
100 hrs, 5d Cost, 5
Yearly
Fixed Total
Cost, 5 Cost, S
Corn- soybeans-wheat-meadow, fall shred, no-till plant (continued)
combine corn
combine soybeans
combine wheat
corn head
platform
hay mower/conditioner
hay rake
hay baler
Total
Notes:
a. Source: Table 3, Appendix C, Control of Hater Pollution
b. Acres on which implement is used each year.
c. Number of trips through field with implement.
d. Computed as percentage of list price. Used two percent
shredder: five oercent for moldboard olow. chisel olow.
479
30
30
479
30
349
30
63
from
62
62
62
62
125
62
62
62
.5
.b
.b
.b
.5
.5
.S
Cropland ,
for combine.
1
1
1
1
1
3.
3.
3.
unless
platform.
66.88
29.38
37.50
5 74.38
5 65.63
5 137.81
otherwise
corn head;
542
156
77
336
75
.00
.00
.00
.00
.00
306.00
noted.
three
^rcent
percent
for hay
362
4b
2ti
249
49
421
for
rake
.49
.83
.88
.92
.22
.70
4706.19
1354.55
668.60
782.00
203.65
979.53
harrow; four
, hay baler;
5068.68
1400.38
697.48
1031.92
252.87
1401.23
13,728.36
percent for stalk
seven percent for
hay nower/conditioner; eight percent for planters, wheat drill. Source: Table 3, Appendix C.
e. See Table A-2 for equipment specifications.
f. Dr. Klaus Alt, ISU, Ames, Iowa.
g. Midwest Farm Planning Manual, p. 142.
h. Purdue Crop Budget, p. 30.
-------
Table A-4. Tractor Costs
Tractor hours per acre were calculated by summing the hours
per acre given in Table A-5 for each machine pulled by" a tractor for
each practice considered. The disk and harrow were assumed to move
over the field in tandem for all alternatives where they are used,
and to average 1.5 times over the field annually for the C-B chisel
plow option. Additional times over the field were also counted for
the haying operations such that each time an operation is carried
out (i.e. mowing, raking, baling) tractor usage is increased. The
corn head and platform are attachments to the combine and so their
hours per acre were not included. For the rotation options, hours
per acre figures were adjusted for some implements prior to summing,
to reflect the fact that they are crop-specific and not used in all
years of the rotation (the "acres of use" column, Table A-3, accounts
for this adjustment factor).
Of the 0.2 hours per acre added for fertilizer application,
0.1 is for N and 0.1 for P and K application. For the corn-bean
rotation, fertilizer is only applied once every two years so only
0.1 hours per acre were added. For the CBWM option, 0.125 hours
per acre were added because N, P, K are applied once for corn and
beans and once for wheat and K is applied once for the meadow.
Other calculations were completed as indicated in the foot-
notes. List prices are averages of local dealer estimates.
Economic life was estimated using information from the Midwest
Farm Planning Manual based on the total annual tractor hours for
each option.
90
-------
Table A-4. Tractor Costs
I ten
Tillage Practices
C Conv. C Chisel C No-till
Rotations
CB Conv. CB Chisel CB No-till Part
CBHM
. No-till
CBHM
No-till, Herb.
Terraces
C Conv. C Chisel
CB No-till
Tractor hours
oer acre" 1.72 1.59
Total tractor
hours 473.00 437.25
Tractor initial
costs, $C 23,600.00 23,600.00
Economic life,
yearsd 12 12
Salvage value,
percent® 25.5 25.5
Yearly depreci-
ation, $ 1,465.17 1,465.17
Taxes , insurance
t housing, $f 1,062.00 1,062.00
Average annual
interest, $' 1,184.72 1,184.72
Total fixed
costs, S 3,711.89 3,711.89
Repair costs,
sh 893.02 825.53
Total tractor costs,
$ (excl. fuel) 4,604.91 4,537.42
Notes: c « corn; CB - corn-bean; CBWM
1.28
352 . 00
23,600.00
13
23.5
1,388.77
1,062.00
1,165.84
3,616.61
664 . 58
4,281.19
1.54
347.27
23,600.00
13
23.5
1,388.77
1,062.00
1,165.84
3,616.61
655.65
4,272.26
1.32
363.00
23,600.00
13
23.5
1,388.77
1 , 062 . 00
1,165.84
3,616.61
685.34
4,301.95
= corn-bean-wheat-meadow.
a. Assume tractor is required for harvest hauling in amount equivalent
to tine requirements for combine-. Add 0.2 hours for application of
fertilizer with rented implements.
b. Increased by 10 percent for idling, travel to field, etc.
c. 100 PTO hp diesel (average of local Indiana equipment dealer
1.01
277.75
23,600.00
14
21.5
1,323.29
1,062.00
1,146.96
3,532.25
524.39
4,056.64
1.97
541.75
23,600.00
12
25.5
1,465.17
1 , 062 . OO
1,184.72
3,711.89
1,022.82
4,734.71
1
508
23,600
12
25
1,465
1,062
1,184
3,711
960
4,672
.85
.75
.00
.5
.17
.00
.72
.89
.52
.41
1.72
473.00
23,600.00 23,
12
25.5
1,465.17 1,
1,062.00 1,
1,184.72 1,
3,711.89 3,
893.02
4,604.91 4,
1
437,
600.
12
25,
465.
062
184.
711.
825.
537.
.59
.25
.00 23,
.5
.17 1,
.00 1,
.72 1,
.89 3,
.53
.42 4,
1.01
277.75
600.00
14
21.5
323.29
062.00
146.96
532.25
524.39
056.64
e. From Appendix C, Table 4, used values corresponding to appropriate
economic life.
f. Taxes 2%, insurance 1.5% of initial cost, Purdue Crop Budget, p. 22;
housing 1%, Appendix C, Table 2.
q. 8% per year, £ur.d.u.e Crop Budget, p.
22; yearly cost =[(I+S)/2]r.
price estimates).
d. From Sidney James (ed.) Midwest Farm Planning Manual, 3rd
edition. Revised Printing, ISU Press, Ames, Iowa, 1975,
Table 4.7, p. 129.
h. 0.8% of list price per 100 hours of use. Appendix C, p. 182.
-------
Table A-5. Fuel Costs
Fuel costs were based on cost per hour for total tractor and
combine hours. Tractor fuel costs were estimated according to a
standard formula, 0.044 times the maximum PTO hp. Combine fuel
costs were more complicated to estimate since data on fuel consump-
tion are only available on a per acre basis and vary according to
the crop being harvested. The formula used was gal./acre x I/
(hours per acre) x $0.50/gal. x 1.15 (for lubrication costs). For
the corn-soybean rotations and the corn-soybean-wheat-meadow rota-
tions the results using the above formula for each crop were
averaged.
92
-------
Table A-5. Fuel Costs
Item
Tillage Practices
C Conv. C Chisel C No-till
Rotations
CBWM
CB Conv. CB Chisel CB No-till Part. No-till
CBWM
No-till, Herb.
Terraces
C Conv. C Chisel
CB No-till
Total tractor
hours
Fuel cost per trac-
tor hour, $a
Tractor fuel
coat, S
Total combine
hours
Fuel cost per ,
combine hour,S
Combine fuel
cost, $
Total fuel
cost, $
473.00
2.53
1,196.69
117.50
1.96
230.30
1,426.99
437.25
2.53
1,106.24
117.50
1.96
230.30
1,336.54
352.00
2.53
890.56
117.50
1.96
230.30
1,120.86
347.27
2.53
878.59
96.25
2.03
195.39
1,073.97
363 . 00
2.53
918.39
96.25
2.03
195.39
1,113.78
277.75
2.53
702.71
96.25
2.03
195.39
898.10
541. 75
2.53
1,370.63
66.88
2.06
137.77
1,508.40
508.75
2.53
1,287.14
66.88
2.06
137.77
1,424.91
473.00
2.53
1,196.69
117.50
1.96
230.30
1,426.99
437.25
2.53
1,106.24
117.50
1.96
230.30
1,336.54
277.75
2.53
702.71
96.25
2.03
195.39
898.10
Notes: C * corn; CB - corn-bean; CBWM = corn-bean-wheat-meadow.
a. Fuel consumption (diesel) gallons per hour = 0.044 x PTO hp. Lubrication costs at 15% of fuel cost, George E. Ayres, Estimating Farm
Machinery Costs, ISO Cooperative Extension Service, Ames, Iowa, November 1976, p. 8. Assume diesel fuel at $0.50/gal., Purdue Crop
Budget, p. 24.
b. Fuel consumption (diesel) corn - 1.60 gal./acre; beans, wheat = 1.10 gal./acre, George E. Ayres, Fuel Required for Field Operations,
ISU Cooperative Extension Service, Ames, Iowa, May 1976, p. 2. Lubrication at 15% of fuel cost. Assume diesel fuel at $0.50/gal.,
Purdue Crop Budget, p. 24.
-------
Table A-6. Seed Costs
Seed costs are calculated from the estimated amounts of seed
applied per acre and the price of seed per pound or bushel. Seed-
ing rates for corn vary according to soil type and tillage practice.
Wheat and hay seed amounts are constant for the two tillage prac-
tices involving them. Soybean seed amounts are increased for reduced
tillage, but are insensitive to soil type.
Seed cost per acre is calculated as the average for all years
of the rotation, if not continuous corn. Total seed cost is deter-
mined for the whole farm based upon the average annual seed cost
and the total acres farmed.
94
-------
Table A-6. Seed Costs
I tea
Tillage Practices
C Conv. C Chisel C No-till
Rotations
CB Conv. CB Chisel CB No-till
CBHM
Part. No-till
CBHM
No-till, Herb.
Terraces
C Conv. C Chisel
CB Ni-tlll
cn
Seeding rate
(seeds/acre)
A uplands 20,OOO 20,000 21,000 20,000 20.OOO 21,000
B ridge 22,000 22,000 23,000 22,000 22,000 23,000
C lowlands 24,000 24,000 25,000 24,000 24,000 25,000
Seed amount, bu.°/acre
A uplands .238 .238 .250 .238 .238 .250
B ridge .262 .262 .274 .262 .262 .274
C lowlands .286 .286 .298 .286 .286 .298
Seed cost, $7acre
A uplands 9.52 9.52 10.00 9.52 9.52 10.00
B ridge 10.48 10.48 10.96 10.48 10.48 10.96
C lowlands 11.44 11.44 11.92 11.44 11.44 11.92
Wheat
Seed amount, bu.'vacre
Seed cost, SVacre
Hay
Seed amount, Ibs. /„,-,-„
Seed cost. $9 /.or.
Soybeans
Seed amount, bu.h/acre 1-00 1.00 1.05
Seed cost, SVacre 10.80 10.80 11.34
Seed cost per
acre , S*
"JTuplands 9.52 9.52 10.00 10.16 10.16 10.67
B ridge 10.48 10.48 10.96 10.64 10.64 11.15
r lowlands 11.44 11.44 11.92 11.12 11.12 11.63
20.0OO
22,000
24,000
.238
.262
.286
9
10
11
1
7
14
18
1
11
11
11
12
.52
.48
.44
.5
.13
.00
.12
.05
.34
.53
.77
.01
21,000
23,000
25,000
.250
.274
.298
10
10
11
1
7
14
18
1
11
11
11
12
20 , OOO 20 , 000
22,000 22,000
24,000 24,000
.238 .238
.262 .262
.286 .286
.00 9.52 9.52
.96 10.48 10.48
.92 11.44 11.44
.5
.13
.00
.12
.05
.34
.65 9.52 9.52
.89 10.48 10.48
.13 11.44 11.44
21,000
23,000
25,000
.250
.274
.298
10.00
10.96
11.92
1.05
11.34
10.67
11. IS
11.01
Notes (see following page)
-------
Table A-6 (continued)
Item
Tillage Practices
C Conv. C Chisel
Total seed
cost, $
A upland 2,380.00 2,380.00
B ridge 2,620.00 2,620.00
C lowlands 2,860.00 2,860.00
Notes; C • corn; CB - corn-bean; CBHM
C No-till
Dotations
CB Conv.
2,500.00 2,540.00
2,740.00 2,660.00
2,980.00 2,780.00
CB Chisel
2,540.00
2,660.00
2,780.00
CB No-till Part
2,667.50
2,782.50
2,907.50
CBHM
. No-till
2,882.50
2,942.50
3 , 002 . 50
CBWM
No-till, Herb.
2,912
2,972
3,032
Terraces
C Conv.
50 2,380.00
50 2,620.00
50 2,860.00
C Chisel
2,380.00
2,620.00
2.860.00
CB No-till
2,667.50
2,782.50
2,907.50
" corn-bean-wheat-meado*.
»• Based on discussions with Dr. Donald Griffith, Purdue University; Rex Journey, Allen County Soil Conservation District.
b. Based on 84,000 seeds per bushel. Appendix C, Table 6.
VD c. Assume price of $40 per bushel, Adler's Seed, Kokomo, Indiana.
-------
Table A-7. Fertilizer Costs
Fertilizer costs are calculated from the estimated pounds per
acre application of N, P2°5 and K2° ^or corn' soybeans, wheat and
hay, and the price per pound of these fertilizers. Fertilizer
application rates for corn vary according to soil type and tillage
practice. Application rates are based upon discussions with the
individuals indicated in the footnotes and represent normal expected
application rates for the Black Creek area. Lower yields are expec-
ted on the poorer upland soils and also less N fertilizer is normally
applied. However, more P00 is applied there. Ten percent more N is
& o
used for all no-till alternatives. P90c applications for wheat and
soybeans on the uplands are increased in the same proportion as for
corn. Wheat yields are not expected to vary according to location
(soil type) or tillage practice and, therefore, N application for
wheat is constant. Since this is assumed to be a well-managed farm,
K_0 is applied to the hay as well as the other crops. Soybeans in
the corn-bean rotation are expected to contribute 10 pounds of N per
acre to the corn. Legumes in the corn-bean-wheat-meadow rotation
are expected to contribute 50 pounds of N per acre. These fertilizer
application rates are appropriate for the Black Creek area.
For the rotations, average annual fertilizer amounts are calcul-
ated and the prices applied to these figures. Total fertilizer cost
is determined for the whole farm based upon these annual costs.
Total fertilizer costs include the rental of application equipment.
For calculating equipment rental costs it is assumed that the P and
K for the soybeans are applied along with the corn fertilizer in the
97
-------
corn year for corn-soybean rotation alternatives and also that N is
not applied to hay for corn-soybean-wheat-hay alternatives, so
equipment costs are correspondingly reduced.
98
-------
Table A-7. Fertilizer Costs
I tea
Tillage Practices
C Conv. C Chisel C No-till
Rotations
CBWM CBWH
CB Conv. CB Chisel CB No-till Part. No-till No-till, Herb.
Terraces
C Conv. C Chisel CB No-till
VD
Corn
N , Ibs/acre
A uplands
B ridge
C lowlands
PjOs > Ibs/acre
A uplands
B ridge
C lowlands
125
160
160
44
40
40
125
160
160
44
40
40
137. 5d
176
176
44
40
40
115b
150
150
44
40
40
115b
150
150
44
40
40
126. 5d
165
165
44
40
40
75°
110
110
44
40
40
82. 5d
121
121
44
40
40
125
160
160
44
40
40
125
160
160
44
40
4O
126. 5d
165
165
44
40
40
K2O, Ibs/acre
50
50
Hav.
K2O, Ibs/acre
120
120
Wheat
N, Ibs/acre
60
60
P2O5, Ibs/acre
A uplands
B ridge
C lowlands
44
40
40
44
40
40
K2O, Ibs/acre
40
-------
Table A-7 (continued)
o
o
««* C Conv. C Ch
Soybeans
tiOf, Ibs/acre
A uplands
B ridge
C lowlands
Average Annual
aaount, Ibs/acre
N
A uplands 125
B ridge 160
C lowlands 160
A uplands 44
B ridge 40
C lowlands 40
MO 50
Cost of fertilizer
per acre, $•
A uplands 29.11
» ridg« 32.90
C lowlands 32.90
isel C No-till CB Conv. CB Chisel CB
11 11
10 10
10 10
70 70
125 137.5 57.5 57.5
160 176 75 75
160 176 75 75
44 44 27.5 27.5
40 40 25 25
40 40 25 25
50 50 60 60
29.11 30.74 18.11 18.11
32.90 34.98 19.90 19.90
32.90 34.98 19.90 19.90
Rotations
«"» CBWM
No-till Part. No-till No-till, Herb
11 11 11
10 10 10
10 10 10
70 70 70
63.25 33.75 35.63
82-5 42.5 45.25
82.5 42.5 45.25
27.5 24.75 24.75
25 22.5 22.5
25 22.5 22.5
60 70 70
18.85 15.39 15.63
20.88 16.11 16.46
20.88 16.11 16.46
11
10
10
70
125 125 63.25
160 160 82.5
160 160 82.5
44 44 27.5
40 40 25
40 40 25
50 50 60
29.11 29.11 18.85
32. 9O 32.90 20.88
32.90 32.90 20.88
-------
Table A-7 (continued)
Item
Tillage Practices
C Conv. C Chisel C Mo-till
Rotations
CBHM
CB Conv. CB Chisel CB No-till Part. No-till
CBHM
No-till, Herb.
Terraces
C Conv. C Chisel
CB No-till
Total cost of
fertilizer, $
A uplands
B ridge
C lowlands
Rental of appli-
cation equipment,
ff
Total fertilizer
costs , $
A uplands
B ridge
C lowlands
7277.50
8225. OO
8225.00
262.50
7540.00
8487.50
8487.50
7277.50
6225.00
8225.00
262.50
7540.00
8487. SO
8487.50
7685.00
8745.00
8745.00
262.50
7947 . 50
9007.50
9O07.50
4527.50
4975.00
4975.00
131.25
4658.75
5106.25
5106.25
4527.50
4975.00
4975.00
131.25
4658.75
5106.25
5106.25
4712.50
5220.00
5220.00
131.25
4843.75
5351.25
5351.25
3847 . 50
4027.50
4027 . 50
153.13
4000.63
4180.63
4180.63
3907 . 50
4115.00
4115.00
153.13
4060.63
4268.13
4268.13
7277.50
8225.00
8225.00
262 . 50
7540.00
8487.50
8487 . 50
7277.50
8225.00
8225.00
262.50
7540.00
8487.50
8487.50
4712.50
5220.00
5220.00
131.25
4843.75
5351.25
5351.25
Notes: C • com; CB • corn-bean; CBMM - corn-bean-wheat-meadow.
a. Fertilizer amounts based on discussions with Dr. Harry Galloway, Dr. Donald Griffith, Purdue University and Rex Journey, Allen County
Soil Conservation District.
b. Misuses 10 Ib credit from soybeans to corn, from discussions with Dr. Harry Galloway, Purdue University.
c. Assumes 50 Ib credit from legumes to corn, from discussions with Dr. Harry Galloway, Purdue University.
d. Assume 10 percent increase in N application for all no-tillage alternatives, from discussions with Dr. Harry Galloway, Purdue University.
e. Assume N as NH . Prices per Ib are $0.13 for N, $0.19 for
0 , and $O.O9 for K20, Purdue Crop Budget.
f. Assume $0.70/acre for NH knife and $0.35/acre for 4-ton bulk spreader. Appendix C, Table 7 figures updated using USDA equipment price
index from 1974 to 1976 of 1.415.
-------
Table A-8. Pesticide Costs
Pesticide costs were calculated based on recommended applications
of appropriate herbicides and insecticides for the soil types, tillage
practices and rotations considered. The following factors were accounted
for:
-No tillage options require more herbicides because no cultivation
is used to destroy weeds.
-The corn-bean-wheat-meadow alternative in which the corn is
planted directly into the sod requires an additional type of
herbicide to kill the remaining hay.
-A different herbicide combination is used for corn than soybeans.
-The corn-soybean rotation is assumed to prevent a corn rootworm
problem but increases the likelihood of a cutworm problem.
-Cutworm has a higher probability in no tillage options due to the
amount of residue remaining.
-Wireworm may be a problem where meadow is part of a rotation.
-Insecticides are not generally applied to soybeans.
For all the options considered, a risk averse farmer is assumed,
who applies pesticides when there is a likelihood that they will be
needed. In actuality, the use of the insecticides,particularly, will
vary from farm to farm depending on local conditions.
Using current prices, cost per acre for each crop was calculated
and then multiplied by the number of acres which would be in that crop
in the rotation. Total cost is the sum of the costs for each crop.
102
-------
Table A-8. Pesticide Costs
o
CO
Tillage
I tea C Conv. C Ch
Corn, Amount*
Herbicide* (Lasso-
Atrex costo.), qt
A uplands 3. SO
B ridge 3.00
C lowlands 4.0O
Herbicide (Para-
-------
Table A-8. (Continued)
Tillage
Ite" C Conv. c Ch
isel c No-till CB Conv. CB Chisel
Corn, Cost (continued)
Insecticide, $/acr« d 9.31 9.31 18.36 9 05
A uplands 5705.00 5705.00 11.285.00 2820.00
8 *i*9« 5225.00 5225.00 10.637.50 2580 00
C lowlands 6187.50 6187.50 11.930.00 3061.25
Soybeans, Cost
Herbicide. S/acreC
A uplands
B ridge
C lowlands
Total cost, $
A uplands
B ridge
C lowlands
Total Pesticide
Costs, $
13.76
10.46
16.90
125
1720.00
1307 . 50
2112.50
A uplands 5705.00 5705.00 11,285.00 4540. OO
B ridge 5225.00 5225.00 10,637.50 3887.50
C lowlands 6187.50 6187.50 11.930.00 5173.75
9.05
2820.00
2580.00
3061.25
13.76
10.46
16.90
1720.00
1307.50
2112.50
4540- 00
3887 . 50
5173.75
Not<>: c " corn' CB - corn-bean , CBNH - corn-bean-wheat-meadow.
a. Herbicide types and application rates based on discussions with Dr.
b. Insecticide types and application rates based on discussions with Dr
"•»-te illative it WAS Assunod that All insecticide p(
r** M^nxvaX COStS . T]T6AtBGnt I OX* ObSGlTVecl dABAQG And I
Rotations
— . Terraces
CBHM CBHM
CB No-till Part. No-till Nn-t-ill Hrrh f- r™, ,-,-. ,
*'" "«•"»• — <- ionv. c Chisel rb ::j-til!
9.05 17.14 17.14 9^31 g 31 9 Q5
4193.75 1915.63 2556.25 5705.00 5705.00 4193 75
3913.75 1795.63 2436.25 5225.00 5225.00 3913 75
4478.75 2036.25 2676 88 6187 SO fc]87 SO 4478*75
"-95 14.95 14.95 14 95
!!•« 11.49 11.49 u'49
18.24 18.24 18.24 18 ".24
125 62.5 62.5 125
1868.75 934.38 934.38 1868 75
1436.25 718.13 718.13 H36 25
2280.00 1140.00 1140.00 2280*00
6062.50 2850.01 3490.63 5705. OO 5705. OO 6062.50
5350.00 2513.76 3154.38 5225.00 5225.00 5350.00
6758.75 3176.25 3816 88 6187 50 6187 50 6758 75
Janes Williams, Purdue University.
. Thomas Turpin, Purdue University and Dr. David Pimental, Cornell Universi
.
c. L..so$4.05/lb active, Atrex 53.67/qt, Paraquat S8.75/qt, 2-4-D ester Sl-50/pt, Indianapolis Fan. Bureau prices. Spring, 1977.
d. Furadan $7/lb active. Counter S6.08/bl active. Indianapolis Farm Bureau; Lorsban »1.02/lb. Do- Chemical Co., Indianapolis, Indiana.
e. Lasso.$4.05/qt, Sencor $17.60.1b, Indianapolis Farm Bureau.
-------
Table A-9. Labor Costs
Labor costs are calculated from direct labor hours plus overhead and
hourly labor wage rates. The direct labor hours are the sum of total
tractor hours plus total combine hours. The overhead rate covers general
farm overhead costs in addition to labor overhead. An average farm wage
rate for Indiana was used.
105
-------
Table A-9. Labor Costs
Item
Tillage Practices
C Conv. C Chisel C No-till
CBHM CBHM
CB Conv. CB Chisel CB No-till Part. No-till MO- Mil H...-K
Total direct
labor, hours* 590.50 554.75 469.50 464.77 459:25 374 00 600 63 s,b 63
590.50 554.75 374.00
Overhead (30%),
hours
177.15 166.43
Total labor, hours 767.65 721.18
M
O
cn
Cost per hour, $
Total labor
costs, S
Notes; C - corn;
a. Tractor hours
b. Indiana Crop e
2.80 2.80
2149.42 2019.30
CB - corn-bean; CBWM =
plus combine hours.
md Livestock Statistics
140.85 139.43 137.78 112.20 182.59 172 69 177 IS IAA^I
610 -3S W4.20 597.03 486.20 7i>1.22 748.32 767.65 721 18
2.80 2.80 2.80 2.80 2.80 2 80 2 an ? ep
^708.98 1691.76 1671.68 1361.36 2215.42 2095.30 2149 42 2019 30
corn-bean-wheat-meadow .
_, Purdue University Agricultural Experiment station, A-iqust 1977. Table 89. "Farm u*o» B*t0* - ,
.y P H
average of Field and Livestock Workers and Machine Operators.
-------
Table A-10. Other Costs
Corn drying costs were estimated from the expected crop yield
and the costs of elevator drying. It was assumed that all the corn
harvested would require an average of ten points of moisture removed.
It was assumed that soybeans did not require drying.
Interest on operating capital was calculated for each item of
expense based on an annual interest rate of 8-1/2 percent. Except for
fertilizer and labor costs the interest was charged for the period
indicated on the table for each item.
Fertilizer costs were divided into nitrogen costs which were
assumed to be carried for about twelve months and phosphorous and
potash costs which were carried approximately eight months. The
actual calculation was done as follows: Fertilizer costs x 8/12 x
.085 x 1.35 (factor to account for differences in capital carrying
time) - Fertilizer cost - .0765 = Interest on Operating Capital for
Fertilizer.
Interest on operating capital for labor is based on a variable
labor force over the year; for example, additional labor required during
harvesting is not included in the interest calculation. The calculation
was carried out as follows: (Tractor hours - harvest hours) x 2.80 x 3/12
x .085 x 1.46 (adjustment factor) = Interest on Operating Capital for Labor.
Total other costs are the sum of drying costs and interest costs.
107
-------
Table A-10. Other Coats
o
00
I ten
Corn Drying
Tillage Practices
C Conv.
Grain harvested, bu.
A uplands 26,250
B ridge 32,500
C lowlands 32.500
Cost per bu. , $• n if,
Total cost
A uplands
B ridge
C lowlands
4,200
5,200
5,200
Interest on -Operating
Capital"
Fertilizer (8 BO.)
A uplands 576.81
B ridge 649.29
C lowlands 649.29
Seed (8 so.)
A uplands
B ridge
C lowlands
Pesticide (6 BO.)
A uplands
B ridge
C lowlands
Fuel (3 so.)
Labor (3 BO.)
Total Interest
A uplands
B ridge
C lowlands
Total Other Costs
A uplands
B ridge
C lowlands
134.87
148.47
162.07
242.46
222.06
262.97
30.32
30 88
1,015.34
1,081.02
1,136.53
5,215.34
6,281.02
6,336.53
C Chisel
26,250
32,500
32 , 500
0 16
4,200
5,200
5,200
576.81
649.29
649.29
134.87
148.47
162.07
242.46
222.06
262.97
28.40
27 78
1,010.32
1,076.00
1,131.51
5,210.32
6,276.00
6,331.51
C No-till
24,937.50
32 , 500
26,OOO
0 16
3,990
5,200
4,160
607.98
689.07
639.07
141.67
155.27
168.87
479.61
452.09
507.03
23.82
1,273.45
1,340.62
1,409.16
5,263.45
6,540.62
5,569.16
notations
CB Conv.
13,781.25
17.062.50
17.O62.5O
2,205
2,730
2,730
356.39
390.63
390.63
143.93
150.73
157.53
192.95
165.22
219.88
22.82
737.90
751.21
812.67
2,942.90
3,481.21
3,542.67
CB Chisel
13,781.25
17,062.50
17,062.50
2,205
2,730
2,730
356'. 39
390.63
39O.63
143.93
150.73
157.53
192.95
165.22
219.68
23.67
740.11
753.42
814.88
2,9(15.11
3,483.42
3,544.88
CBUN
CB No-till Part. No-till
13,781.25
17,062.50
15,356.25
2,205
2,730
2,457
370.55
409.37
409.37
151.16
157.96
164.76
257.66
227.38
287.25
19.08
814.22
829.56
896.23
3,019.22
3,559.56
3,353.23
7,218.75
8,937.50
8,937.50
1.155
1,430
1,430
306.05
319.82
319.82
163.34
166.74
170.14
121.13
106.83
134.99
32.05
17.12
639.69
642.56
674.12
1,794.69
2,072.56
2.104.12
CBHH
No-till, Herb.
Terraces
C Conv.
7,218.75 28,000
8,937.50 34,250
8,490.63 34,250
0.16
1,155
1,430
1,358.50
310.64
326.51
326.51
16O.22
163.62
167.02
148.35
134.06
162.22
30.28
14.25
663.74
668.72
700.26
1,818.74
2,098.72
2.058.78
O.16
4,480
5,480
5,480
576.81
649.29
649.29
134.87
148.47
162.07
242.46
222.06
262 . 97
30.32
30.88
1,015.34
1,081.02
1,136.53
5,495.34
6,561.02
6,616.53
C Chisel
28,000
34,250
34,250
0.16
4,480
5,480
5,480
576.81
649.29
649.29
134.87
148.47
162.07
242.46
222.06
262 . 97
28. «
27.78
1,010.32
1,076.00
1,131.51
5,490.32
6,556.00
6,611.51
CB No-till
14,656.25
17,937.50
0.16
2,345
2,870
2 597
370.55
409.37
409.37
151.16
157.96
164 76
257.66
227.38
287.25
19.08
15.77
814.22
829.50
896.23
3,159.22
3,699.56
3,493.23
Notes: C • corn; CB - corn-bean; CBHH = corn-bean-wheat-»eadow.
a. Elevator drying costs for corn for 10 pts. renoved, Purdue Crop Budget, p. 6.
b. Assuse interest at 8.5 percent, Purdue Crop Budget, p. 8.
-------
Table A-11. Revenue
Gross revenue was calculated from the expected yield per acre for
each crop, the number of acres planted with each crop and the expected
price. Expected yields for corn and soybeans vary according to soil
type and farming practice. Crops on wetter soil types do not respond
as well to decreased tillage as on other soils. Lower yields are
expected on the poorer upland soils for all tillage practices. Rotations
tend to increase corn yields. Hay yields are responsive to soil types
whereas wheat yields are not. Tillage practices for wheat and hay do
not vary for the two rotations using them and so yields are not affected.
These yields are appropriate for the Black Creek area. The addition of
terracing was assumed to create better drainage and to allow one week
earlier planting time with yield advantage of one bushel per day.
It should be noted that gross revenue is, of course, very sensitive
to the crop prices chosen.
109
-------
Table A-ll. Revenue
tillage
Ite» C Conv. C Ch
Corn
Expected yield.
bu/acre *
Practices
isel C No-till CB Conv.
A uplands 105 105 99.75 no 25
B ri(J9e 130 130 130 136 50
C lowlands 130 130 104 136.50
Total output, bu
A uplands 26,250 26,250 24,937.50 13,781.25
8 ridge 32,500 32,500 32,500 17,062 5O
C lowlands 32,500 32,500 26,000 17,067 25
CB Chisel
110.25
136.50
136.50
13,781.25
17,062.50
17,062.50
Rotations
CB No-till Pa
110.25
136.50
122.85
13,781.25
17,062.50
15,356.25
CBWM CBHH
rt. No-till No-till, Herb.
115.50 115.50
143 143
143 135.85
62.50 62.50
7,218.75 7,218.75
8,937.50 8,937.50
8,937.50 8 49O 63
c
112 112 117.
137 137 143.
250 250 125
28,000 28,000 14,656.
34,250 34,250 17,937.
25
50
25
50
?/bub 22222 2
A uplands 52,500 52,500 49,875 27,562.50
B ridge 65,000 65,000 65,000 34,125
C lowlands 65,000 65,000 52,000 34,125
Soybeans
Expected yield.
bu/acre*
A uplands
B ridge
C lowlands
Area cropped, acres
Total output, bu
A uplands
B ridge
C lowlands
Expected price.
S/bub
Gross Revenue , $
A uplands
B ridge
C lowlands
30
40
40
3,750
5,000
5,000
5
18,750
25,000
25,000
27,562.50
34,125
34,125
30
40
36
3,750
5,000
4,500
5
18,750
25,000
22,500
27,562.50
34,125
30,712.50
27
38
32
3,375
4,750
4,000
5
16,875
23,750
20,000
14,437.50 14,437.50
17,875 17,875
17,875 16 981 26
27 27
38 38
32 32
62.5 62.5
1,687.50 1,687.50
2,375 2,375
2,000 2,000
5 5
8,437.50 8,437.50
11,875 11,875
10,000 10,000
56,000 56,000 29,312.
68,500 68,500 35,875
29
40
3,625
5,000
-
18,125
25 OCO
21,250
50
-------
Table A-ll (continued)
Item
Tillage Practices
C Conv. C Chisel C No-till
Rotations
CB Conv. CB Chisel CB No-till
CBHH
Part. No-till
CBHH
No-till, Herb.
Terraces0
C Conv. C Chisel
CB No-till
Wheat
Expected yield.
bu/acre a
Area cropped, acres
Total output, bu
Expected price.
$/bu°
Gross Revenue, $
tav.
Expected yield.
tons/acre a
A uplands
B ridge
C lowlands
Area cropped, acres
Total output, tons
A uplands
B ridge
C lowlands
Expected price.
$/tond
Gross Revenue, $
A uplands
B ridge
C lowlands
TOTAL GROSS
REVENUE, $
A uplands 52,500 52,500 49,875 46,312.50 46,312.50 44,437.50
B ridge 65,000 65,000 65,OOO 59,125 59,125 57,875
C lowlands 65,000 65,000 52,000 59,125 59,125 50,712.50
Notes: C » corn; CB = corn-bean; CBWM = corn-bean-wheat-meadow.
45
62.50
2,812.50
2.50
7,031.25
3.50
4
4
62.50
218.75
250
250
60
13,215
15,000
15,000
43,031.25
51,781.25
49,906.25
45
62.50
2,812.50
2.50
7,031.25
3.50
4
4
62.50
218.75
250
250
60
13,125
15,000
15,OOO
43,031.25 56,000 56,000 47,437.50
51,781.25 68,500 68,500 60,875
49,012.50 68,500 68,500 53,712.50
a. Yield levels based on discussions with Dr. Harry Galloway and Dr. Donald Griffith, Purdue University. Yield reductions for no-till
are preliminary and »ay change with more information. No-till yields are highly dependent on soil type and weed control.
b. Purdue Crop Budget, Department of Agricultural Economics, Purdue University, Lafayette, Indiana, 1977, p. 7.
d.
7 bu/acre corn yield advantage with terracing due to better drainage, 2 bu/acre soybean yiald advantage.
Based on discussions with Rex Journey, Allen County Soil Conservation District.
-------
Table A-12. Summary
This table is straightforward. All costs were added for each
farming practice alternative and then subtracted from gross revenue to
give net return. Land costs were not included since these were assumed
to be the same for each soil type no matter what farming practice is
used, it should be noted, however, that when we eliminated land costs
from the summary calculation we eliminated a variable which might tend
to equalize return among farmers located on different soils. For example,
an upland farm may have much lower land costs than a lowland farm which
might counterbalance the differences in net return. Due to the use of
a percentage factor added to labor costs to cover farm overheads as well
as to the elimination of land costs, the net revenue values are most
useful for relative comparisons among alternatives rather than as measures
of actual profit.
112
-------
Table A-12. Suaury
I tea
Tillage Practice*
C Con*. C ChiMl C Mo-till
Botations
OHM
CB Conv. CB ChiMl CB no-till Part. Mo-till
cm*
No-till. Herb.
Terraces
C COB", C Chisel
CB No-till
Gross
B ridge
C lowlands
Costs
Tractor (excl .
fuel)
iBpleaants
(excl. fuel)
Fuel
Seed
B ridge
C lowlands
Fertilizer
A uplands
B ridqe
Pesticides
A uplands
B ridge
Labor
Terracing
Other
A uplands
B ridge
52,500
65,000
65,000
4.604.91
10,643.65
1.426.99
2,380
2,620
2,860
7,540
8,487.50
8.487.50
5,705
5,225
6,187.50
2.149.42
5,215.34
6,281.02
6,336.53
Total Cost (Net of
Land Cost)
A uplands 39,665.31
B ridge 41,438.49
C lowlands 42,696.50
Net Return (Excl
Land Costs)
A uplands
B ridge
C lowlands
-
12,834.69
23,561.51
22,303.50
52,500
65,000
65,000
4.537.42
10,365.66
1,336.54
2,380
2,620
2,860
7,540
8,487.50
8,487.50
5,705
5,225
6,187.50
2.019.30
5,210.32
6,276
6.3:1.51
39,094.24
40,867.42
42,125.43
13,405.76
24,132.58
22,374.57
49,875
65,000
52.0OO
4,281.19
8,913.07
1,120.86
2,500
2,740
2,980
7,947.50
9,007.50
9.007.50
11,285
10,637.50
11,930
1,708.98
5,263.45
6,540.62
5,569.16
43,020.05
44,949.22
45,510.76
6,854.95
20,050.78
6,489.24
46,312.50
59,125
59,125
4,272.26
11,134.31
1,073.97
2,540
2,660
2,780
4,658.75
5,106.25
5,106.25
4,540
3,887.50
5,173.75
1,691.76
2,942.90
3,481.21
3,542.67
32,853.95
33,307.01
34,774.97
13,458.55
25,817.99
24,350.03
46,312.50
59,125
59,125
4,301.95
10,828.87
1,113.78
2,540
2,660
2,780
4,658.75
5,106.25
5,106.25
4,540
3,887.50
5,173.75
1,671.68
2,945.11
3,483.42
3,544.88
32,600.14
33,053.45
34.521.16
13,712.36
26,071.55
24,603.84
44,437.50
57,875
50,712.50
4,056.64
9,376.28
898.10
2,667.50
2,787.50
2.907.50
4,843.75
5,351.25
5,351.25
6,062.50
5,350
6,758.75
1,361.36
3,019.22
3,559.56
3.352.23
32,285.35
32,740.69
34.063.31
12.152.15
25,134.31
16,649.39
43,031.25
51,781.25
49,906.25
4.734.71
14,493.38
i. son. 40
2,882.50
2,942.50
3,002.50
4,000.63
4,180.63
4,180.63
2,850.01
2,513.76
3,176.25
2.215.42
1,794.69
2,072.56
2.104.12
31,479.74
34,661.36
35,415.43
8,551.51
17,119.89
14,490.82
43,031.25
51,781.25
49,012.50
4,672.41
13,728.36
1.424.91
2,912.50
2,972.50
3,032.50
4,060.63
4,268.13
4,268.13
3,490.63
3,154.38
3,816.88
2-095.30
1,818.74
2,098.72
34,203.48
34,414.71
35,097.27
8,827.77
17,366.54
13,915.23
56,000
68,500
68,500
4,604.91
10,643.65
1,426.99
2,380
2,620
2,860
7,540
8,407.50
8,487.50
5,705
5,225
6,187.50
2,149.42
5,495.34
6,561.02
6.616.53
46,405.31
48,178.49
49,436.50
9,594.69
20,321.51
19,063.50
56,000
68,500
68,500
4,537.42
10,365.66
1,336.54
2,380
2,620
2,860
7,540
8,487.50
8.487.50
5,705
5,225
6,187.50
2,019.30
5,490.32
6,556
6.611.51
45,834.24
47,607.42
48,365.43
10,165.76
20,892.58
19,634.57
47,437.50
60.875
53.712.50
4.056.64
9.376.28
898.10
2.667.50
2,787.50
2,907.50
4,843.75
5,351.25
5.351.25
6,062.50
5,350
6,758-75
1,361.36
6,460
3,159.22
3,699.56
3.493.23
38,885.35
39,340.69
40,663.11
8,552.15
21,534.31
13,049.39
C - corn; CB » corn-bean; CBHM - corn-bean-wheat-meadow.
-------
Table A-13. Net Revenue Ranking
Table A-13 shows the ranking of the farming practice options
according to net revenue, from the highest revenue producing alternative
to the lowest. The rankings are shown for each soil type and also for
all- soil types simultaneously.
For all soil types the corn-soybean chisel plow option is the best,
better than conventional tillage although only slightly better. Since
gross revenues are the same for both of these options, the difference
is caused by the slightly lower equipment costs for the chisel plow
option (see Table A-12, Summary).
It is interesting to note that the corn-soybean rotation options
using chisel and conventional tillage produce more revenue than continuous
corn. This is not primarily due to a favorable corn-soybean price ratio as can
be seen from the gross revenue rows in Table A-12. The difference is caused,
in large part, by the higher fertilizer and pesticide costs which the
addition of soybeans in the rotation helps to reduce. Labor hours are
also a factor because harvesting soybeans is quicker than harvesting corn.
The no-till options, for both the corn-soybean rotation and continuous
corn, produce less revenue (much less for continuous corn on the uplands
and lowlands) than conventional or chisel tillage. This is caused by
two factors, a lower yield combined with high pesticide costs. The
extra pesticide is needed to kill weeds which are more abundant due to
lack of plowing and to eradicate insects which the residue tends to
encourage. The no-tillage options are more suited to better drained soils
as illustrated by the very good yield for the corn-soybean no-tillage option
114
-------
for the ridge soils.in Table A-ll and the correspondingly high net revenue ranking.
The corn-soybean-wheat-meadow rotation options produce less revenue
than the corn-soybean and continuous corn options, generally. Even though
many costs such as for pesticides are lower for these options and though
corn yields are quite high (see Table A-ll), the loss of revenue from put-
ting half the acreage into wheat and hay instead of corn or corn and
soybeans is so great that the net return for these options is low.
Equipment costs are also very high for these rotation options (see
Alternative A).
The terrace options produce lower net revenue than the other options
because the cost of installing terracing is not outweighed by the yield
advantage gained by improved drainage. The terrace options follow the
same pattern as the non-terraced options, chisel plowing being more
lucrative than conventional tillage and that in turn better than no-
tillage except for the ridge farm where the yield advantage of the better
drained soils makes this option more attractive.
When all soils are considered together it can be seen that the
ridge soils, generally speaking, produce the most revenue, although
there is not much of a difference between ridge and lowland soils for
conventional and chisel tillage. The small differences between these
soils for these two tillage practices is caused by the slightly higher
seed and pesticide cost borne by the lowland farms. When the no-tillage
practice is employed there is a greater difference in yields between
the ridge and lowland soils, caused mainly by the lowered yields on the
lowlands. The upland soils are much poorer than the other two soils and
115
-------
are associated with a much lower yield resulting in consistently lower
net revenues for all fanning practices except no-tillage on the lowlands.
This practice is just not suited to a wet, poorly-drained soil, so its
poor performance is reflected in a very low net return.
high
low
Table A-13. Net Revenue Ranking
Ridge
Lowlands
All Soils
h CB Chisel CB Chisel CB Chisel r
CB Conv. CB Conv. CB Conv. r
C Chisel CB No-till C Chisel r
C Conv. C Chisel C Conv. 1
CB No- till C Conv. C Chisel-Ter 1
C. Chisel-Ter. CB No-t.-Ter. C Conv.-Ter. r
C Conv.-Ter. c Chisel-Ter. CB No-till r
CBWMr-Herb. c Conv.-Ter. CBWM-Part. 1
CB No-t.-Ter. c No- till CBWM-Herb. 1
CBWM-Part. CBWM-Herb CB No-t.-Ter. r
C No-till CBWM-Part. C No-till r
r
r
1
1
r
r
1
1
1
u
u
u
1
u
u
u
u
u
u
u
u
1
«- 27
CB Chisel
CB Conv.
CB No-till
CB Chisel «- 25
CB Conv.
C Chisel
C Conv. «. 23
C Chisel
C Conv.
CB No-till-Ter.^21
C Chisel-Ter.
C Conv.-Ter.
C NO-till ^ 2Q
C Chisel - Ter.
C Conv.-Ter.
CBWM-Herb.
CBWM-Part. «- 17
CB No-till
CBWM-Part. ^ , .
•<- 14
CBWM-Herb.
CB Chisel
CB Conv.
C Chisel
CB No-till-Ter. 13
C Conv. "*"
CB No-till
C Chisel-Ter.
•*• 10
C Conv.-Ter.
CBWM-Herb
CB No-till-Ter.
CBWM-Part. ^_ fi
C No-till
C No-till , ,
•*- 6
,000
f V *^\S
,000
,000
,000
,000
,000
,000
,000
,000
,000
,000
Notes: C = corn; CB = corn-bean; CBWM = corn-bean-wheat-meadow.
r = ridge; 1 = lowlands; u = uplands.
116
-------
Table A-14. Soil Loss Ranking
Table .A-14 shows the farming practice options ranked according to level
of soil loss, expressed in tons per acre, from low losses to high losses.
As one would expect, the corn-soybean-wheat-meadow options with half the
acreage in a grass cover crop have the lowest soil losses for each of the
soil types considered. The partially plowed CBWM option loses more soil
than the herbicide option since plowing turns under the meadow sod. The
no-tillage practice lowers runoff because more residue remains to retain
the water. Terracing is a structural measure which prevents water from
flowing off the field as quickly as it otherwise would. Chisel plow options
also produce less soil loss than conventional tillage options since more
residue remains after chisel plowing than after moldboard plowing which
turns the soil completely over. Soil loss on the corn-soybean rotations
is higher than on the continuous corn options because soybean residue is
not as bulky as corn residue.
Taking all the soils together and ranking the farming practices,
shows that, as one would predict, soil loss is greatest for the more
erosive upland soils with the greatest slope, less for the ridge, and
lowest for the lowlands which have almost no slope. The range of soil
loss is quite large, going from less than one ton per acre lost from the
corn-soybean-wheat-meadow option on the lowlands to almost 28 tons per
acre from the conventionally tilled corn-soybean rotation on the uplands.
As indicated in the footnote on Table A-14, the column showing tons
per acre of soil lost from the farming practice options can be used to
visualize the effects of a soil loss restriction policy. If a limit
117
-------
were set at two tons per acre, for example, then all the practices ranked
below that limit would not be allowed. This policy would have an unequal
effect on farms depending on where they are located. It would force all
farms located on the uplands and ridge to move to a meadow rotation (this
conclusion assumes, of course, that all rotation possibilities available
to the farmer have been considered in our ranking). Referring back to
Table A-13, Net Revenue Ranking, it can be seen that the farm located on
the lowlands would make out the best in terms of profit under such a
policy. In fact, farmers owning lowlands would probably experience wind-
fall gains in the short term since their land would become relatively much
more valuable. Such a farmer could still use his most profitable option,
a chisel plowed corn-soybean rotation. Farmers on the ridge would be
forced to switch to one of ther lowest net revenue options; they would
lose the most revenue under such a policy. Farmers on the uplands would
also lose revenue by switching to a less profitable option. Although they
would have the lowest net revenue under this soil loss restriction policy,
they also made less in the unrestricted case.
118
-------
Upland
Table A-14
Soil Loss Ranking
Lowland
All Soils
Low CBWM-Herb. CBWM-Herb. CBWN-Herb. 1
CBWM-Part. CBWM-Part. CBWM-Part. r
C No-till C No-till C No-till 1
CB No-t.-Ter. CB No-t.-Ter. CB No-t.-Ter. 1
C Chisel-Ter. C Chisel-Ter. C Chisel-Ter. 1
CB No-till CB No-till CB No-till 1
C Chisel C Chisel C Chisel u
CB Chisel CB Chisel CB Chisel 1
C Conv.-Ter. C Conv.-Ter. C.Conv.-Ter. 1
C Conv. C Conv. C Conv. r
high CB Conv. CB Conv. CB Conv. 1
1
r
r
r
1
1
r
r
u
r
r
u
u
u
r
r
u
u
u
u
u
u
CBWM-Herb .
CBWM-Herb.
CBWM-Part
C No-till
CB No-till-Ter^
C Chisel-Ter.
CBWM-Herb .
CB No-till
C Chisel
CBWM-Part.
CB Chisel
C Conv.-Ter.
C No. till
CB No-till-Ter.
C Chisel-Ter.
•«-
C Conv.
CB Conv.
CB No-till
•«-
C Chisel
CBWM Part. ^_
•<-
CB Chisel
C Conv.-Ter.
C No-till ^
•«-
CB No-till-Ter.
C Chisel-Ter. ,
•<-
C Conv.
CB Conv.
CB No-till
C Chisel
CB Chisel
•<-
C Conv.-Ter.
C Conv.
CB Conv. ,
•4-
1 ton*
2 ton
3 ton
4 ton
5 ton
8 ton
9 ton
10 ton
16 ton
28 ton
Notes: C = corn; CB = corn-bean; CBWM = corn-bean-wheat-meadow.
r = ridge; 1 = lowlands; u = uplands.
* If soil loss restrictions of the tonnages given per acre were imposed,
then only the farming practices on the soils indicated located above the arrow
would be permissible.
119
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Alternative A: Custon Wheat, Hay
This alternative was designed to examine the effects of using
custom operations instead of purchasing wheat and hay equipment. It
was chosen because it appeared that the base case assumption, that a
farmer moving to a corn-soybean-wheat-hay rotation would purchase
specialized equipment for planting wheat and harvesting hay, was some-
what unrealistic. This is especially true since the hay is only grown
on one quarter of the farm acreage. In fact, the farmer most probably
would hire in help and equipment to carry out these operations for him.
This was the assumption made in the "Alternative A tables.
Table A.-3A lists the equipment used in the two corn-sdybean-wheat-
hay options along with the custom operations and their costs which
would be substituted for some of the equipment in the base case example.
The rates listed are averages for Northern Indiana and come from the
Cooperative Extension Service. The table shows the total equipment and
custom operation cost for each alternative which may be compared with
the totals in Table A-3.
The total tractor hours for the custom alternatives would not be
the same as for the two base case wheat, hay options because of the
equipment changes discussed above. Fewer tractor hours would be required
to haul fewer implements. Table A-4A shows the altered tractor hour per
acre figure and traces the resulting tractor cost charges. Fuel cost
would be similarly affected and this is shown in Table A-5A. Labor costs
are dependent on tractor hours and are therefore also lowered with the
addition of the custom operations. This is illustrated in Table A-9A.
120
-------
Talble A-3A. Machinery Costs — Custom Wheat, Hay Alternative
Item
Corn- soybeans-wheat-meadow, fall turn-plow corn, fall
shred, no-till plant others
stalk shredder
mold board plow
disk
harrow
sprayer
no-till planter
combine corn, soybeans, wheat
corn head
platform
custom drilling wheat and meadow
custom hay mowing/conditioning, one operation
custom raking hay
custom baling hayc — uplands
— ridge and lowlands
Total — uplands
— ridge and lowlands
Corn- soybeans-wheat-meadow, fall shred, no-till plant
stalk shredder
disk
harrow
sprayer
no-till planter
combine corn, soybeans, wheat
corn head
platform
custom drilling wheat and meadow
custom hay mowing/conditioning, one operation
custom raking hay
custom baling hay — uplands
— ridge and lowlands
Total — uplands
— ridge and lowlands
a. From Table 3.
h Source: Indiana Custom Rates, EC-130 (Rev.), Cooperative
Total
non-custom
cost, $a
509.04
742.52
1,219.26
123.31
262.51
1,080.09
5,068.68
1,400.38
697.48
-
-
-
:
11,103.27
11,103.27
509.04
1,198.16
121.91
262 . 51
1,080.09
5,068.68
1,400.38
697.48
-
_
-
10,338.25
10,338.25
Custom
cost per
acre, $b
—
-
-
-
_
-
-
-
-
3.63
5.63
2.66
25.34
28.97
37.26
40.89
.
-
-
—
-
-
-
3.63
5.63
2.66
25.34
28.97
37.26
40.89
Extension Service, Purdue
Custom
acres
—
-
-
-
_
-
-
-
-
62.5
62.5
62.5
62.5
62.5
62.5
62.5
_
-
-
_
-
-
-
62.5
62.5
62.5
62.5
62.5
62.5
62.5
University,
Total
Custom
cost, $
-
-
-
_
—
—
—
-
226.88
351.88
166.25
1,583.75
1,810.63
2,328.75
2,555.63
-
-
_
-
—
—
226.88
351.88
166.25
1,583.75
1,810.63
2,328.75
2,555.63
Total
cost, $
509.04
742.52
1,219.26
123.31
262.51
1,080.09
5,068.68
1,400.38
697.48
226.88
351.88
166.25
1,583.75
1,810.63
13,432.02
13,658.90
509.04
1,198.16
121.91
262.51
1,080.09
5,068.68
1,400.38
697.48
226.88
351.88
166.25
1,583.75
1,810.63
12,667.00
12,893.88
West Lafayette, Indiana, 19
Rates given are average 1976 prices for Northern Indiana; Black Creek Watershed is in Northern Indiana.
Custom hay baling rate from above source is $0.21 per 58 Ib bale so rates given vary according to yield variations.
-------
Table A-4A
Tractor Costs — Custom Wheat, Hay Alternative
Item
a
Tractor hours per acre
Total tractor hours
Tractor initial costs, $
Economic life, years
Salvage value, percent
Yearly depreciation, $
Taxes, insurance & housing, $
b
Average annual interest, $
Total fixed costs, $
Repair costs, $
Total tractor costs, $
(excluding fuel)
CBWM
Partial
No-till
.79
217.25
23,600.00
14
21.5
1,323.29
1,062.00
1,146.96
3,532.25
410.17
3,942.42
CBWM
No-till
Herbicide
.68
187.00
23,600.00
15
19.5
1,266.53
1,062.00
1,128.08
3,456.08
353.06
3,809.14
Notes; CBWM = corn-bean-wheat-meadow.
a. Tractor hours per acre from Table A-4, minus hours per acre for
implements replaced by custom operations.
b. See footnotes to Table A-4.
122
-------
Table A-5A
Fuel Costs — Custom Wheat, Hay Alternative
Item
a
Total tractor hours
Fuel cost per tractor hour, $
Tractor fuel cost, $
Q
Combine fuel cost, $
Total fuel cost, $
CBWM CBWM
Partial No-till
No-till Herbicide
217.25 187.00
2.53 2.53
549.64 473.11
137.77 137.77
687.41 610.88
Notes: CBWM = corn-bean-wheat-meadow.
a. From Table A-^5.
b. See footnotes to Table A-5.
c. Derivation shown in Table A-5.
Table A-9A
Labor Costs — Custom Wheat, Hay Alternative
Item
Total direct labor, hours*
Overhead (30 percent), hours
Total labor, hours
Cost per hour, $*
Total labor costs, $
CBWM
Partial
No- till
284.13
85-^4
369.37
2.80
1,Q34.24
CBWM
No-till
Herbicide
253.88
76.16
330.04
2.80
924.11
Notes: CBWM = corn-bean-wheat-meadow.
* See footnotes to Table A-9. Tractor hours from Table A-4A.
123
-------
Since fuel and labor costs have been decreased, interest on operating
capital for financing these input factors is correspondingly decreased,
as shown in Table A-lpA.
Table A-12A summarizes all the changes discussed above and shows a
new net revenue figure for each corn-bean-wheat-hay rotation. Hiring in
custom operators yields approximately a 45 percent increase in revenue
for a farm located on the upland soils and about an 24 percent increase
for a farm on the ridge or lowland soils.
The increase in net revenue produced by substituting custom oper-
ations for purchase of certain equipment results in an improvement in
position of the two wheat, hay rotations in comparison to the other
farming practices considered. If Table A-13A is compared with Table A-13,
Net Revenue Ranking, it can be seen that the CBWM options move up on
the ranking list for each soil type, from 7 and 9 to 5 and 6 for the
uplands farm, from 10 and 11 to 7 and 9 for the ridge and from 8 and 9
to 7 and 8 for the lowlands. In the ranking for all soils, the highest
CBWM option moves from the sixteenth to the eleventh spot. It can be
concluded from this comparison that although the substitution of
custom operations for the purchase of wheat and hay equipment certainly
improves the attractiveness of this rotation option in comparison to
the more common farming practices, it alone does not improve net revenue
enough to put it in a competitive position.
124
-------
Other Costs -
Table A-10A
- Custom Wheat, Hay Alternative
Item
Corn drying Costs
A uplands
B ridge
C lowlands
b
Interest on Operating Capital
Fuel (3 months)0
Labor (3 months)
Other interest6
A uplands
B ridge
C lowlands
Total interest
A uplands
B ridge
C lowlands
Total Other Costs
A uplands
B ridge
C lowlands
CBWM
Partial
No-till
1155
1430
1430
14.61
13.06
590.52
593.39
624.95
618.19
621.06
652.62
1,773.19
2,051.06
2,082.62
CBWM
No-till
Herbicide
1155
1430
1358.50
12.98
10.43
619.21
624.19
655.75
642.62
647.60
679.16
1,797.62
2,077.60
2,037.66
Notes: CBWM = corn-bean-wheat-meadow.
a. Derivation shown in Table A-10.
b. See footnotes to Table A-10.
c. Fuel costs from Table A-5A.
d. Labor costs, from Table A-9A.
e. From Table A-10.
125
-------
Table A-12A
Summary — Custom Wheat, Hay Alternative
Item
CBWM
Partial
No-till
CBWM
No-till
Herbicide
Gross revenue, $*
A uplands
B ridge
C lowlands
Costs
Tractor (excluding fuel)**
Implements (excluding fuel)***
A uplands
B ridge
C lowlands
Fuel"1"
Labor"1"1"
Drying and interest costs+++
A uplands
B ridge
C lowlands
Other Costs*
A uplands
B ridge
C lowlands
Total cost (net of land cost)
A uplands
B ridge
C lowlands
Net return (excluding land costs)
A uplands
B ridge
C lowlands
43,031.25
51,781.25
49,906.25
3,942.42
13,432.02
13,658.90
13,658.90
687.41
1,034.24
1,773.19
2,051.06
2,082.62
9,733.14
9,636.89
10,359.40
30,602.42
31,010.92
31,764.99
12,428.83
20,770.33
18,141.26
43,031.25
51,781.25
49,012.50
3,809.14
12,667.00
12,893.88
12,893.88
610.88
924.11
1,797.62
2,077.60
2,037.66
10,463.76
10,395.01
11,117.51
30,272.51
30,710.62
31,393.18
12,758.74
21,070.63
17,619.32
Notes: CBWM = corn-bean-wheat-meadow.
* From Table A-12.
** From Table A-4A.
*** From Table A-3A.
+ From Table A-5A.
++ From Table A-9A.
+++ From Table A-10A.
126
-------
Table A-13A
Net Revenue Ranking — Custom Wheat, Hay Alternative
Uplands
Lowlands
All Soils
high CB Chisel CB Chisel CH Chisel r
CB Conv. CB Conv. CB Conv. r
C Chisel CB No-till C Chisel r
C Conv. C Chisel C Conv. 1
CBWM-Herb. C Conv. C Chisel-Ter. 1
CBWM-Part. CB No.-t.Ter. C Conv.-Ter. r
CB No-till CBWM-Herb. CBWM-Part. r
C Chisel-Ter. C Chisel-Ter. CBWM-Herb. 1
C Conv.-Ter. CBWM-Part. CB No-till 1
CB No-t.-Ter. C Conv.-Ter. CB No-t.Ter. r
low C No-till C No-till C No-till r
r
r
r
r
1
1
1
1
1
u
u
u
1
u
u
u
u
u
u
u
u
1
CB Chisel
CB Conv.
CB No-till *"
CB Chisel
CB Conv.
C Chisel
C Conv.
C Chisel
C Conv.
CB No-till Ter.
CBWM-Herb.
C Chisel-Ter.
CBWM-Part.
C Conv.-Ter.
C No-till ^_
C Chisel-Ter.
C Conv.-Ter.
CBWM-Part.
CBWM-Herb.
CB No-till
CB Chisel "*~
CB Conv.
C Chisel
CB No-t.-Ter. ^
•«-
C Conv.
CBWM-Herb.
CBWM-Part .
CB No-till
C Chisel-Ter.
«-
C Conv.-Ter.
CB No-t.-Ter.
•«-
C No-till
C No-till
«-
27,000
25,000
23,000
21,000
20,000
15,000
13,000
10,000
8 ,000
6,000
Notes; C = corn; CB = corn-bean; CBWM = corn-bean=wheat-meadow.
r = ridge; 1 = lowlands; u = uplands.
127
-------
Alternative B: Energy Cost Increase
Alternative B assumes a future scenario in which energy prices
have increased while other costs have remained constant. The B Alter-
native examines the effects of this cost increase on the farmer's factors
of production and on his net return.
Table A-5B illustrates the method used to develop the energy price
increase. Tractor fuel cost and combine fuel cost per hour have been
increased by a factor of 2.068. This factor was derived from the annual
price change rates for the years 1977 through 1985 for crude oil (refiner
acquisition). The source of these projections is Energy Review, Summer
1977, published by Data Resources, Inc., Lexington, Massachusetts. Total
fuel cost was calculated in the same way for Table A-5B as for Table A-5.
Table A-7B shows how fertilizer costs have been increased. A different
price increase factor was used for each type of fertilizer depending
upon the relative amounts of different energy inputs used in its produc-
tion. It was assumed that other inputs to the production of fertilizer
such as marketing, administration, and labor were either a very small
component of the total cost or would move proportionally to the energy
cost. Therefore the price of the fertilizer to the farmer was assumed
to increase at the same rate as that of the energy inputs to fertilizer
production. (This same assumption was made for pesticide costs, corn
drying costs, and fuel costs.) Sources of the percentages of energy
inputs to fertilizer production are given in the footnotes to Table A-7B.
The energy price increase factors were developed from projections from
the same source as for the fuel increase factor, above. Energy input
126
-------
Table A-5B. Fuel Costs — Increased Energy Cost Alternative
I tea
Tillage Practices
C Conv.
Total tractor hours 473.00
Fuel cost per trac-
tor hour, S* 5.23
Tractor fuel
cost, $
Total combine
H-1 hours
Jg Fuel cost per
coobine hour, $*
Conbine fuel
cost, $
Total fuel
cost, $
2474.75
117.50
4.05
476.26
2951.02
C Chisel
437.25
5.23
2287.70
117.50
4.05
476.26
2763.96
Notes: C * corn; CB * corn-bean; CBUH
C No-till
352.00
5.23
1841.68
117.50
4.05
476.26
2317.94
Rotations
CB Conv.
347.27
5.23
1816.92
96.25
4.20
404.07
2220.97
CB Chisel
363.00
5.23
1899.23
96.25
4.20
404.07
2303.30
CBMM
CB No-till Part. No-till
277.75
5.23
1453.20
96.25
4.20
404.07
1857.27
541.75
5.23
2,833.35
66.88
4.26
284.91
3,118.26
CBNH
No-till, Herb.
508.75
5.23
2.660.76
66.88
4.26
284.91
2.945.67
Terraces
C Conv.
473.00
5.23
2474.75
117.50
4.05
476.26
2951.02
C Chisel
437.25
5.23
2287.70
117.50
4.05
476.26
2763.96
CB No-till
277.75
5.23
1453.20
96.25
4.20
404.07
1857.27
« corn -bean-wheat-meadow .
* For derivation see footnotes. Table A-5. Assume 1985/1977 price ratio of 2.068, developed from annual price
change data for crude oil (refiner acquisition) from Energy Review, Summer 1977, Data Resources Inc., Lexington,
Massachusetts.
-------
Table A-7B.
Fertilizer Costs - Increased B»ergy Cost Alternative
U)
o
Xtea
Tillage Practices
C Conv.
C Chisel
C Ho-till
Rotations
CB Conv.
CB Chisel
CB No-till Pai
CBMM
-t. No-till K
CBHH
Average annual Fertilizer ' " ,
aaount , Ibs/acre*
N
A uplands
B ridge
C lowlands
PjOs
A uplands
B ridge
C lowlands
KzO
125
160
160
44
40
40
5O
Cost of Fertilizer**
N
A uplands
B ridge
C lowlands
PjOs
A uplands
B ridge
C lowlands
KiO
Cost of Fertilizer
acre, $
A uplands
B ridge
C lowlands
Total cost of
Fertilizer, $
34.92
44.70
44.70
17.06
15.51
15.51
9 22
per
61.20
69.43
69.43
A uplands 15,300
B ridge 17,357.50
C lowlands 17,357.50
125
160
160
44
40
40
34.92
44.70
44.70
17.06
15.51
15.51
61.20
69.43
69.43
15,300
17,357.50
17,357.50
137.50
176
176
44
40
40
38.42
49.17
49.17
17.06
15.51
15.51
64.70
73.90
73.90
57.50
75
75
27.50
25
25
16.06
20.96
20.96
10.67
9.69
9.69
37.79
41.71
41.71
16,175 9,447.50
18,475 10,427.50
18,475 10,427.50
57.50
75
75
27.50
25
25
16.06
20.96
20.96
10.67
9.69
9.69
37.79
41.71
41.71
9,447.50
10,427.50
10,427.50
63.25
82.50
82.50
27.50
25
25
17.67
23.05
23.05
10.67
9.69
9.69
39.40
43.80
43.80
9,850
10,950
10,950
33.75
42.50
42.50
24.75
22.50
22.50
9.43
11.88
11.88
9.59
8.74
8.74
31.92
33.52
. 33.52
7,980
8,380
8,380
35.63
45.25
45.25
24.75
22.50
22.50
9.96
12.64
12.64
9.59
8.74
8.74
12.90
32.45
34.28
34.28
125
160
160
44
40
40
50
34.92
44.70
44.70
17.06
15.51
__.. 15.51
9.22
61.20
69.43
69.43
8,112.50 13,300
8,570 17,357.50
8,570 17,357.50
125
160
160
44
40
40
50
34.92
44.70
44.70
17.06
15.51
15.51
9.22
61.20
69.43
69.43
15,300
17,357.50
17,357.50
63.25
82.50
27.50
25
25
17.67
23.05
23.05
10.67
9.69
9.69
11.06
39.40
43.80
43.80
9,85O
10,950
10 , 950
-------
Table A-7B (continued)
Item
Tillage Practices
C Conv. C ChiMl C HO-till
Rotation*
CBHM
CB Conv. CB ChiMl CB No-till Part. No-till
CBHM
No-till, Herb.
Terraces
C Conv. C Chliel
CB No-till
Rental of appli-
cation equipment,
$*
Total Fertilizer
Costs, $
A uplands
B ridge
C lowlands
262.50
15,562.50
17,620
17,620
262.50
15,562.50
17,620
17,620
262 . 50
16,437.50
18,737.50
18,737.50
131.25
9,578.75
10,558.75
10,558.75
131.25
9,578.50
10,558.75
10,558.75
131.25
9,981.25
11,081.25
11,081.25
153.13
8,133.13
8,533.13
8,533.13
153.13
8,265.63
8,723.13
8,723.13
262.
15,562.
17,620
17,620
.50 262.50
.50 15,562.50
17,620
17,620
131.25
9,981.25
11,081.25
11,081.25
Motes: C » cornj CB - corn-bean; CBWN * corn-bean-wheat-meadow.
• For derivation see Table A-7B; see footnotes. Table A-7B.
*• Cost of fertilizer derived from fertilizer prices from Table 7 multiplied by the following 1985/1975 price ratios: N — 2.149; PjOs —
2.041; KiO — 2.048. Fertilizer price ratios are produced by multiplying energy input amounts by energy input price ratios. Energy inputs
to N: 95% natural gas; 5% electricity (Source: Davis, C. H. and G. M. Blouin, "Energy Consumption in the U.S. Chemical Fertilizer System
from the Ground to the Ground," p. 321 in W. L. Lockertz (ed.), Agriculture and Energy, Academic Press, New York, 1977.) Energy inputs to
PjO5: 18% oil, 69% natural gas. 111 electricity (percents developed from data in White, W. C. and K. T. Johnson, Energy Requirements for the
Production of Phosphate Fertilizers, Draft, The Fertilizer Institute, Washington, D.C. (no date)). Energy inputs to KaO: 81* natural gas,
11% electricity (percents developed from data in White, W. C., "Fertilizer-Food-Energy Relationships," paper presented at the American Chemical
Society Division of Fertilizer and soil Chemistry, Chicago, Illinois, August 28, 1973). 1985/1977 price ratios for natural gas (industrial),
electrictiy (Marginal industrial), and crude oil (refiner acquisition) of 2.185, 1.462, and 2.068, respectively, developed from annual price
change data from Energy Review, Summer 1977, Data lesources, Inc., Lexington, Massachusetts.
-------
percentages were multiplied by energy price increase factors and then
summed to obtain the price increase factor for each type of fertilizer.
Pesticide cost increases are given in Table A-8B and were calculated
in the same way as fertilizer cost increases. All pesticide costs are
assumed to increase by the same factor, 2.013, since the percentages of
energy inputs are assumed to be the same for all. The source of this
information is listed in the footnote to Table A-8B which also lists the
energy price increase factors and their source.
Corn drying costs (Table A-10B) are increased due to increased energy
cost. Off-farm corn drying is based on energy from LP gas and natural
gas. A price increase ratio of 2.127 was used for corn drying. The first
footnote to Table A-10B lists the sources of data from which this figure
was calculated. Table A-10B"also" shows increased interest costs necessary
to support more operating capital needed to finance the increased ferti-
lizer, pesticide and fuel expenses which the farmer encounters in this
scenario.
Table A-12B summarizes. Lhe energy cost increase alternative showing
higher fuel, fertilizer, pesticide and "other" costs. Total costs in
Table A-12B when compared with Table A-12 have increased from between §$0,000
and $30,000 or 30 to 65 percent. These high cost increases, of course,
affect net return drastically. As the "net return" figures indicate,
many options are no longer financially viable.
Table 'A-13B shows how increased energy costs have affected the ranking
of the options in terms of net revenue. Only 11 out of 33 options produce
a positive return, and one of these is below $1,000. Farmers on the up-
132
-------
Table A-8B. Pesticide Costs — Increased Enerqy Cost Alt*rn»nw«
Item
Tillage Practices
C Conv. C Chisel C No-till
Dotations
CBHM
CB Conv. CB Chisel CB No-till Part. No-till
CBHM
No-till, Herb.
Terraces
C Conv. C Chisel
CB No-till
CO
Ul
CORK
Total Herbicide and
Insecticide Cost, $*
A uplands
B ridge
C lowlands
SOYBEAN
Total Herbicide
Cost, $•
A uplands
B ridge
C lowlands
Total Pesticide
Cost, $
A uplands
B 'ridge
C lowlands
11,484.17
10,517.93
12,455.44
11,484.17
10,517.93
12,455.44
11,484.17
10,517.93
12,455.44
11,484.17
10,517.93
12,455.44
22,716.71
21,412.28
24,015.09
22,716.71
21,412.28
24,015.09
5,676.66
5,193.54
6,071.71
3,462.36
2,632
4,252.46
9,139.02
7,825.54
10,414.76
5,676.66
5,193.54
6,071.71
3,462.36
2,632
4,252.46
9,139.02
7,825.54
10,414.76
8,442.16
7,878.38
9,015.72
3,761.79
2,891.17
4,589.64
12,203.81
10,769.55
13,605.36
3,856.
3,614.
4,098.
1,880.
1,445.
2,294.
5,737.
5,060.
6,393.
16
60
97
91
60
82
07
20
79
5,145.73
4,904.17
5,388.56
1,880.91
1,445.60
2,294.82
7,026.64
6,349.77
7,683.38
11,484.17
10,517.93
12,455.44
11,484.17
10,517.93
12,455.44
11,484.17
10,517.93
12,455.44
11,484.17
10,517.93
12,455.54
8,442.02
7,878.38
9,015.72
3,761.79
2,891.17
4,589.64
12,203.81
10,769.55
13,605.36
Notes: C * corn; CB - corn-bean; CBHM « corn-bean-wheat-meadow.
* For derivation of pesticide amounts see Table 8 and footnotes to Table 8. Pesticide costs have been increased using a 1985/1977 price
ratio of 2.013. This price ratio was developed by multiplying pesticide energy input amounts by energy input price ratios. Energy inputs to
the production of pesticides are 42% oil, 38% natural gas, 20% coal (Source: Pinentel, David, Energy Inputs for the Production, Formulation,
Packaging and Transport of Various Pesticides, Draft, November 1977, p. 3). 1985/1977 price ratios for crude oil (refiner acquisition),
natural gas (industrial), and coal (contract) of 2.068, 2.185, and 1.568, respectively, were developed from annual price change data from
Energy Review, Summer 1977, Data Resources, Inc., Lexington, Massachusetts.
-------
Table A-10B.
Others Costs - Energy Cost Increase Alternative
Item
Corn Drying
Tillage Practices
C Conv.
Grain harvested, bu
A uplands 26,250
B ridge 32,500
C lowlands 32,500
Cost per bu, $* 734
A uplands 8,933.40
B ridge 11,060.40
C uplands 11,060.40
Operating Capital**
Fertilizer (8 no.)***
A uplands 1,190.53
B ridge 1,347.93
C lowlands 1,347.93
Seed (8 mo.)
A uplands
B ridge
C lowlands
Pesticide (6 «o.)+
A uplands
B ridge
C lowlands
Fans -*>.)*+
ijaoor (j BO.)
Total Interest
A uplands
B ridge
C lowlands
134.87
148.47
162.07
488.08
447.01
529.36
62.71
30.88
1,906.57
2,037.00
2,132.95
Total Other Costs
A uplands 10,839.97
B ridge 13,097.40
C lowlands 13,193.35
C Chisel
26,250
32 , 500
32,500
.34
8,933.40
11,060.40.
11,060.40
1,190.53
1,347.93
1,347.93
134.87
148.47
162.07
488.08
447.01
529.36
58.73
1,899.49
2,029.92
2,125.87
10,832.89
13,090.37
13,186.27
C No-till
24,937.50
32,500
26,000
34
8,486.73
11,060.40
8,848.32
1,257.47
1,433.42
1,433.42
141.67
155.27
168.87
965.46
910.02
1,020.64
49.26
20.37
2.434.23
2,568.34
2,692.56
10,920.96
13,628.74
11,540.88
Rotations
CB Conv.
13,781.25
17,062.50
17,062.50
4,690.04
5,806.71
5,806.71
732.77
807.74
807.74
143.93
150.73
157.53
388.41
332.59
442.63
47.20
21.81
1,334.12
1,360.07
1,476.91
6,024.16
7,166.78
7,283.62
CB Chisel
13,781.25
17,062.50
17,062.50
4,690.04
5,806.71
5,806.71
732.77
807.74
807.74
143.93
150.73
157.53
388.41
332.59
442.63
48.96
23.17
1,337.24
1,363.19
1,480.03
6,027.28
7,169.90
7,286.74
CB No-till Pai
13,781.25
17,062.50
15,356.25
4,690.04
5,806.71
5,226.04
763.57
847.72
847.72
151.16
157.96
164.76
518.66
457.71
578.23
J974T
15.77
1,488.63
1,518.63
1.645.95
6,178.67
7,325.34
6,871.99
CBWM
•t. No-till
7,218.75
8,937.50
8,937.50
2,456.69
3,041.61
3,041.61
622.18
652.78
652.78
163 . 34
166.74
170.14
243.83
215.06
271.74
, , ,66 -.26
17.12
1,112.73
1,117.96
1,178.04
3,569.42
4,159.57
4,219.65
CBWM
7,218.75 28,000
8,937.50 34,250
8,490.63 34,250
.34 .34
2,456.69 9,528.96
3,041.61 11,655.96
2,889.53 11.655.96
632.32
667.32
667.32
160.22
163.62
167.02
298.63
269.87
326.54
14.25
1,168.02
1,177.66
1,237.73
1,190.53
1,347.93
1,347.93
134.87
146.47
162.07
488.08
447.01
529.36
•6277V
30.88
1,906.57
2,037.00
2,132.95
3,624.71 11,435.53
4,219.27 13,692.96
4, 127. 26 13,788.91
28 , 000
34,250
9,528.96
11,655.96
11,655.96
1,190.53
1,347.93
1,347.93
134.87
148.47
488.08
447.01
529.36
' 58.73
27.78
1,899.49
2,029.92
2,125.87
11,428.45
13,685.88
13,781.83
14,656.25
17,937.50
4,987.82
6,104.49
5,523.82
763.57
847.72
151.16
157.96
518.66
457.71
578.23
1,488.63
1,518.63
1,645.95
6,476.45
7,623.12
7,169.77
Notes: C - corn; CB - corn-bean; CBWM - corn-bean-wheat-meadow.
* For initial price and cost derivation see Table 10. Price and cost have been increased using a 1985/1977 price ratio of 2.127 derived
by multiplying the energy input amounts to off-fam corn drying (SO* LP gas and 5O% natural gas, U.S. Food and Fiber Sector, U.S. Senate Report,
September 1974) by 1985/1977 price ratios for crude oil (refiner acquisition) and natural gas (industrial) of 2.068 and 2.185, respectively.
These price ratios were developed from annual price change data in Energy Review, Summer 1977, Data Resources, Inc., Lexington, Massachusetts.
** See footnotes in Table A-10, * "Fertilizer costs from Table A-7B, +Pesticide costs from Table A-8B,
•n-Fuel costs from Table A-5B.
-------
Table A-12B. Su«ary - Energy Cost Increase Alternative
Item
Tillage
Practices
C Conv. C Chisel C No-till
A uplands 52,500 52,500
B ridge 65,000 65,000
C lowlands 65,000 65,000
Rotations
CB Conv.
49,875 46,312.50
65,000 59,125
52,000 59,125
CB Chisel
46,312.50
59,125
59,125
CB No-till Pa
44,437.50
57,875
50,712.50
CBHM
rt. No-till
43,031.25
51,781.25
49,906.25
CBHM
No-till, Herb.
C Conv.
43,031.25 56,000
51,781.25 68,500
49,012.50 68,500
Terraces
C Chisel
56,000
68 , 500
68,500
CB No-t
47,437
60,875
53,712
ill
.50
.50
Ul
en
Costs
Tractor (excl.
fuel)
Implements
(excl. fuel)
Fuel*
Seed
A uplands
B ridge
C lowlands
Fertilizer**
A uplands
B ridge
C lowlands
Pesticides***
A uplands
B ridge
C lowlands
Labor
Terracing
Other*
A uplands
B ridge
C lowlands
Total Cost (Net
Land Cost)
A uplands
B ridge
C lowlands
Net Return (Excl
Land Cost)
A uplands
B ridge
C lowlands
Notes: C - corn
4,604.91
10,643.65
2,951.02
2,380
2,620
2,860
15,562.50
17,620
17,620
11,484.17
10,517.93
12,455.44
2^149.42
0
10,839.97
13,097.40
13,193.35
of
60,615.64
64,204.33
66,477.79
-8,115.64
795.67
-1,477.79
4,537.42
10,365.66
2,763.96
2,380
2,620
2,860
15,562.50
17,620
17,620
11,484.17
10,517.93
12,455.44
2,019.30
0
10,832.89
13,090.32
13,186.27
59,945.90
63,534.59
65,808.05
-7,445.90
1,465.41
-808.05
; CB - corn-bean; CBHM
4,281.19
8,913.07
2,317.94
2,500
2,740
2,980
16,437.50
18,737.50
18,737.50
22,716.71
21,412.28
24,015.09
1,708.98
0
10,920.96
13,628.74
11,540.88
69,796.35
70,999.70
74,494.65
-19,921.35
-5,999.70
-22,494.65
4,272.26
11,134.31
2,220.97
2,540
2,660
2,780
9,578.75
10,558.75
10,558.75
9,139.02
7,825.54
10,414.76
1,691.76
0
6,024.16
7,166.78
7,283.62
46,601.23
47,530.37
50,356.43
-288.73
11,594.63
8,768.57
4,301.95
10,828.87
2,303.30
2,540
2,660
2,780
9,578.75
10,558.75
10,558.75
9,139.02
7,825.54
10,414.76
1,671.68
0
6,027.28
7,169.90
7,286.74
46,390.85
47,319.99
50,146.05
-78.35
11,805.01
8,978.95
4,056.64
9,376.28
1,857.27
2,667.50
2,787.50
2,907.50
9,981.25
11,081.25
11,081.25
12,203.81
10,769.55
13,605.36
1,361.36
0
6,178.67
7,325.34
6,871.99
47,682.78
48,615.19
51,117.65
-3,245.28
9,259.81
-405.15
4,734.71
14,493.38
3,118.26
2,882.50
2,942.50
3,002.50
8,133.13
8,533.13
8,533.13
5,737.07
5,060.20
6,393.79
2,215.42
0
3,569.42
4,159.57
4,219.65
44,883.89
45,257.17
46,710.84
-1,852.64
6,524.08
3,195.41
4,672.41
13,728.36
2,945.67
2,912.50
2,972.50
2,032.50
8,265.63
8,723.13
8,723.13
7,026.64
6,349.77
7,683.38
2,095.30
0
3.624.71
4,219.27
4,127.26
45,271.22
45,706.41
47,008.01
-2,239.97
6,074.84
2,004.49
4 , 604 . 91
10,643.65
2,951.02
2,380
2,620
2,860
15,562.50
17,620
17,620
11,484.17
10,517.93
12,455.44
2,149.42
6,460
11,435.53
13,692.96
13,788.91
67,671.20
71,259.89
73,533.35
4,537.42
10,365.66
2,763.96
2,380
2,620
2,860
15,562.50
17,620
17,620
11,484.17
10,517.93
12,455.44
2,019.30
6,460
11,428.45
13,685.88
13,781.83
67 , 001 . 46
70,590.15
72,863.61
-11,671.20-11,001.46
-2,759.89 -2,090.15
-5,033.35 -4,363.61
4,056.64
9,376.28
1,857.27
2,667.£.n
2,787.50
2,907.50
9,981.25
11,081.25
11,081.25
12,203.81
10,769.55
13,605.36
1,361.36
6,460
6,476.45
7,623.12
7,169.77
54,440.56
55,372.97
57,875.43
-7,003.06
-5, 307.83
-4,162.93
» corn-bean-wheat-aeadow.
*Fuel costs from Table A-5B, "Fertilizer costs from Table A-7B, ***Pesticide costs from Table A-8B, mother costs
I ITOItl TcLDJ-6 A™ -
-------
Table A-13B
Net Revenue Ranking — Energy Cost Increase Alternative
Uplands
Lowlands
All Soils
high CB Chisel CB Chisel CB Chisel r
CB Conv. CB Conv. CB Conv. r
CBWM-Part. CB No-till CBWM-Part. r
CBWM-Herb. CBWM-Part. CBWM-Herb. 1
CB No-till CBWM-Herb. CB No-till 1
CB No-t.-Ter. C Chisel C Chisel r
C Chisel C Conv. C Conv. r
C Conv. C Chisel-Ter. CB No-t.-Ter. 1
C Chisel-Ter. C Conv.-Ter C Chisel-Ter 1
C Conv.-Ter. CB No-t.-Ter. C Conv.-Ter. r
low C No-till C No-till C No-till r
u
u
1
1
1
u
r
u
r
u
1
1
1
r
r
u
u
u
u
u
u
1
CB Chisel
CB Conv.
CB No-till
CB Chisel
CB Conv.
CBWM-Part .
CBWM-Herb.
CBWM-Part
CBWM-Herb
C Chisel
C Conv
CB Chisel
CB Conv.
CB No-till
C Chisel
C Conv.
CBWM-Part.
C Chisel-Ter.
CBWM-Herb.
C Conv.-Ter.
CB No- till
CB No-t.-Ter.
C Chisel-Ter.
C Conv.-Ter.
CB No-till-Ter.
C No-till
CB No-t.-Ter.
C Chisel
C Conv.
C Chisel-Ter.
C Conv.-Ter.
C No-till
C No-till
+• 12,000
•<- 9 000
•J f W W
•*• 6 000
\J f \J\J\J
+• 2,000
•*- 1,000
«- -3,000
•*• -3,000
•*• —5,000
«• -6,000
«- -9,000
+ -12,000
«- -20,000
•*- -23,000
Notes; C = corn; CB = corn-bean; CBWM = corn-bean-wheat-meadow.
r = ridge; 1 = lowlands; u = uplands.
136
-------
lands no longer have revenue producing options available to them. The
CBWM options are the least energy intensive and their costs increase the
least so they move up in rank for all soil types. On the uplands, they
move from seventh and ninth place to third and fourth place. They also
rank high on the other two soil types moving from tenth and eleventh
place to fourth and fifth place on the ridge and from eighth and ninth
place to third and fourth place on the lowlands when compared to the base
case (Table A-13 ).
Revenue from the corn-soybean rotations, chisel and conventionally
tilled on the ridge and lowlands, is high and their use of energy intensive
factors of production such as fertilizer and pesticides is relatively low
compared to continuous corn, for example, so that these options remain
the most attractive financially. This is also true for the corn-soybean
no-tillage rotation on the ridge soil. In contrast, the continuous corn
options, both conventionally and chisel tilled, use relatively more of the
energy intensive factors of production, enough to negate the effect of
their high gross revenues. The energy price increase in this instance
serves to highlight the natural benefits provided by the soybeans to the
corn in the form of pest control and nitrogen fertilizer credit.
Table A-15B shows the effects of combining the energy price increase
future scenario with alternative A, the use of custom hiring to carry out
certain operations in the corn-soybean-wheat-hay rotation options. The
costs and revenues for the two options displayed in this table offer perhaps
a more realistic picture of the effect of a large energy price increase.
Both options become relatively more attractive financially in comparison
137
-------
Table A-15B.
CBWH Farm Practice with Custom Rate
and 1985 Energy Prices
Tractor
Implements
A
B
C
Fuel
Seed
A
B
C
Fertilizer
A
B
C
Biocides
A
B
C
Labor
Drying & Intr'
A
B
C
Total Cost
A
B
C
Gross Revenue
B
C
Net Revenue
A
B
C
Custom
Option
3,942
13,432
13,659
13,659
687
1,034
t
1,773
2,051
2,083
12,429
20,770
18,141
1977
Non
Custom
4,735
14,493
14,493
14,493
1,508
2,882
2,942
3,002
4,001
4,181
4,181
2,850
2,514
3,176
2,215
1,795
2,073
2,104
34,480
34,661
35,415
8,552
17,120
14,491
R77=
Custom
Non Custom
.833
.927
.943
.943
.456
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
.467
.988
.989
.990
1985
Non
Custom
4,735
14,493
14,493
14,493
3,118
2,882
2,942
3,002
8,133
8,533
8,533
5,737
5,060
6,394
2,215
3,569
4,160
4,220
44,884
45,257
46,711
43,031
51,781
49,906
-1,852
+6,524
+3,195
Custom = R-
x Non
Customg 5
3,942
13,432
13,659
13,659
1,422
2,882
2,942
3,002
8,133
8,533
8,533
5,737
5,060
6,394
1,034
3,526
4,114
4,174
42,985
43,583
45,037
43,031
51,781
49,906
+46
+8,198
+4,869
138
-------
Table A-15B. Continued.
Tractor
Implements
A
B
C
Fuel
Seed
A
B
C
Fertilizer
A
B
C
Biocides
A
B
C
Labor
Drying & Intr
A
B
C
Total Cost
A
B
C
Gross Revenue
A
B
C
Net Revenue
A
B
C
Custom
Option
3,809
12,667
12,894
12,894
611
No
change
No
Change
No
Change
924
•t
1,798
2,078
2,038
30,273
30,711
31,393
12,759
21,071
17,619
1977
Non
Custom
4,672
13,728
13,728
13,728
1,425
2,912
2,972
3,032
4,061
4,268
4,268
3,491
3,154
3,817
2,095
1,819
2,099
2,059
34,203
34,415
35,097
43,031
51,781
49,012
8,828
17,367
13,915
1985
R77=
Custom
Non Custom
.815
.923
.939
.939
.429
)
jl.O
\
\
[l.O
1
1.0
.441
.989
.990
.990
Non
Custom
4,672
13,728
13,728
13,728
2,946
2,912
2,972
2,032
8,265
8,723
8,723
7,027
6,350
7,683
2,095
3,625
4,219
4,127
45,271
45,706
47,008
43,031
51,781
49,012
- 2,240
-1- 6,075
2,004
Custom = R77
x Non
Customs 5
3,809
12,667
12,894
12,894
1,263
2,912
2,972
2,032
8,266
8,723
8,723
7,027
6,350
7,683
924
3,585
4,177
4,086
40,453
41,112
41,414
43,031
51,781
49,012
+ 2,578
+ 10,669
+ 7,598
139
-------
to other practices. The upland farmer, for example, could use the CBWT1
no-till option to produce a positive net return.
It can be concluded from this example that a large energy price
increase would have severe consequences to farmers causing them to switch
to farming practices which are less energy intensive, to relocate or remove
land from production, and to increase use of natural rather than manufac-
tured means of adding nutrients to the soil and of pest control. Note,
however, that the results of this alternative are extreme, and in reality
an energy price increase such as this would have other effects on other
costs and on food prices so that the results would be somewhat different
than those of the simplified case considered here. But this case does
serve to illustrate the direction of the effects of a large energy price
increase.
140
-------
Alternative C: Price Subsidy
Alternative C examines the effect of a price subsidy policy for
wheat. Tables A-3 and A-14 show that although the wheat-hay rotations
produce relatively little soil loss compared to other options, they are
not as attractive in terms of revenue as the continuous corn or the corn-
soybean rotations. In Alternative C, a price subsidy mechanism was
used to make the wheat-hay rotation options more attractive compared to the
highest net revenue producing options in the initial case. The corn-
soybean rotation was already more financially appealing than the continuous
corn option (see Table A-12), so it was not considered useful to examine a
soybean price subsidy.
Table A-11C shows the price of wheat subsidized to $5.00 (a subsidy
of $2.50 per acre) which doubles the gross revenue from the acres planted
with wheat in the wheat-hay rotation options. The total gross revenue
from these options is thus increased by about $7,000 or 15 percent.
The wheat/corn price ratio has been changed from 1.25 to 2.5 and the
wheat/soybean price ratio from 0.5 to 1.0.
Table A-12C shows a relatively higher net return for the two wheat-
hay rotation options compared to the initial case (compare with Table A-12).
Table S-13C indicates how this increased net return has shifted the net
revenue ranking of the CBWM options when compared to Table A-13,"Net
Revenue Ranking. For the uplands they have moved from seventh and ninth
place to first and second, for the ridge from tenth and eleventh to
fourth and fifth and for the lowlands from eighth and ninth to fifth
and sixth. The ranking for all soils shows that the highest revenue
141
-------
Table A-llc. Revenue - Price Subsidy Alternative
Item
Tillage Practices
C Corw. C Chisel C Mo-till
Rotations
CBHM CBWM
CB Conv. CB Oii»«l CB No-till Part. Mo-till no-till. Herb.
Corn
Terraces
C Conv. C Chisel Cfl No-till
Gross Revenue, $*
A uplands 52,500
B ridge 65,000
C lowlands 65,000
Soybeans
Gross Revenue, $*
A uplands
B ridge
C lowlands
Wheat
Expected yield, bu/acre
Gross Revenue. $
52,500 49,875 27,562.50
65,000 65,000 34,125
65,000 52.000 34,125
18,750
25,000
25,000
27,562.50
34,125
34,125
18,750
25,000
22,500
27,562.50
34,125
30,712.50
16,875
23,750
20,000
14,437.50
17,875
17,875
8,437.50
11,875
10,000
14,062.50
14,437.50 56,000 56,000 29,312.50
17,875 68,500 68,500 35,875
8,437.50 18,125
11,875 25,000
10,000 21 250
14,062.50
Hay.
Gross Revenue, $*
A uplands
B ridge
C lowlands
13,125
15,000
13,125
15,000
TOTAL GROSS ' '
REVENUE, $
A
B
C
Not
uplands
ridge
lowlands
52 , 500
65,000
65,000
:orn; CB = corn-
52,500
65,000
65,000
•bean; CBWM = c
49,875
65,000
52,000
:orn-bean->
46,
59,
59,
whe«
312.50
125
125
46,
59,
59,
312.50
125
125
44
57
,437.50 50,062.50
,875 58,812.50
,712.50 '56,937.50
50,062.50 56,000 56,000 47,437.50
58,812.50 68,500 68,500 60,875
Derivation shown in Table A-ll, also see footnotes, Table A-ll
Assumes wheat price subsidized to S5.00 per bushel.
-------
Table A-12C. Sunmary — Price Subsidy Alternative
Item
Tillage Practices
C Conv. C Chisel C No-till
Rotations
CBHM CMM
CB Conv. CB Chisel CB No-till Part. No-till* No-till, Herb.*
Terraces
C Conv. C Chisel
CB No-till
Gross Revenue, $
A uplands
B ridge
C lowlands
52 , 500
65,000
65,000
52,500
65,000
65,000
49,875
65,000
52,000
46,312.50
59,125
59,125
46,312.50
59,125
59,125
44,437.50
57,875
50,712.50
50,062.50
58,812.50
56,937.50
50,062.50
58,812.50
56,043.75
56 , OOO
68,500
68,500
56, OOO
68,500
68,500
47,437.50
60,875
53,712.50
Total Cost
(Net of Land Cost) * *
A uplands
B ridge
C lowlands
Net Return
Land Costs)
A uplands
B ridge
C lowlands
39,665.31
41,438.49
42,696.50
(Excluding
12,834.69
23,561.51
22,303.50
39,094.24
40,867.42
42,125.43
13,405.76
24,132.58
22,874.57
43,020.05
44,949.22
45,510.76
6,854.95
20,050.78
6,489.24
32,853.95
33,307.01
34,774.97
13,458.55
25,817.99
24,350.03
32,600.14
33,053.45
34,521.16
13,712.36
26,071.55
24,60,3.84
32,285.35
32,740.69
34,063.11
12,152.15
25,134.31
16.649.39
34,479.74
34,661.36
35,415.43
15 , 582 . 76
24,151.14
21,522.07
34,203.48
34,414.71
35.097.27
15,859.02
24,397.79
20,946.48
46,405.31
48,178.49
49,436.50
9,594.69
20,321.51
19,063.50
45,834.24
47,607.42
48,865.43
10,165.76
20,892.58
19,634.57
38,385.35
39,340.69
40,663.11
8,552.15
21,534.31
13.049.39
Notes: C - corn; CB - corn-bean; CBHM - corn-bean-vheat-Beadov.
* Increased revenue frost Table A-11C.
Derivation shown in Table A-12.
-------
Table A-13C
Net Revenue Ranking — Price Subsidy Alternative
Uplands
Lowlands
All soils
high C BWM-Herb. CB Chisel CB Chisel
CBWM-Part. CB Conv. CB Conv.
CB Chisel CB No-till C Chisel
CB Conv. CBWM-Herb C Conv.
C Chisel CBWM-Part. CBWM-Part.
C Conv. C Chisel CBWM-Herb.
CB No-till C Conv. C Chisel-Ter.
C Chisel-Ter. CB No-t.-Ter. C Conv.-Ter.
C Conv.-Ter. C Chisel-Ter. CB No-till
CB No-t.-Ter. C Conv.-Ter. CB No-t.Ter.
low C No-till C No-till C No-till
r
r
r
1
r
r
r
1
1
r
1
1
r
r
1
1
1
u
u
u
u
u
1
u
u
u
u
u
u
1
CB Chisel
CB Conv.
CB No-till
CB Chisel
CBWM-Herb .
C Chisel
C Conv.
C Chisel
C Conv.
CB No-till-Ter.
CBWM-Part .
CBWM-Herb.
C Conv.-Ter.
C No-till
C Chisel-Ter.
C Conv.-Ter.
CB No-till
CBWM-Herb .
CBWM-Part.
CB Chisel
CB Conv.
C Chisel
CB No-till.-Ter.
C. Conv.
CB No-till
C Chisel-Ter.
C Conv.-Ter.
CB No-till Ter.
C No-till
C No-till
27,000
25,000
23,000
21 ,000
20,000
15,000
13 000
J. -J f \J\S\S
10,000
8,000
6,000
Notes; C = corn; CB = corn-bean; CBWM = corn-bean-wheat-meadow.
r = ridge; 1 = lowlands; u = uplands.
144
-------
producing CBWM option (on ridge soils) has moved from sixteenth to
fifth place. If the results of the Custom Wheat Hay Alternative (A)
were combined with a wheat price subsidy (Alternative C) then the CBWM
options would become even more attractive financially. It can be con-
cluded, then, that a price subsidy policy could be effective in encour-
aging the use of cropping patterns which have different water quality
impacts than those that would otherwise be used.
145
-------
Alternative D: Fertilizer Tax
The objective of this alternative is to illustrate the effects
of a tax on the use of nitrogen fertilizer. Such a tax policy might
be considered to control level of nitrates in public drinking water
to meet Federal standards.
In Table A-7D a $.07 tax per pound of nitrogen fertilizer was assumed,
raising the cost from $.13 a pound to $.20 a pound. This is a substantial
price increase. Comparing "cost of fertilizer per acre" and "total
cost of fertilizer" in Table A-7D with the same row in Table A-7, the
effect of the tax has been to rai.se fertilizer expenses by about 35 per-
cent for the option using the most nitrogen fertilizer and by about
15 percent for the option using the least. Table A-10D' simply carries
through the impact of the increased fertilizer costs from Table A-7D
on interest costs (compare with Table A-10).
Table "A-12-D summarizes the changes du'e to the fertilizer tax,
including increased fertilizer and interest (other) costs. A compari-
son with Table A-12 shows that net 'return ha's been significantly decreased,
by $3300 for the options using most nitrogen and by about $800 for the options
using least nitrogen. This is a reduction in net return of 50 percent
for the continuous corn, no-tillage option on the lowlands.
Table T\-13D when compared with Table A-13, Net Revenue Ranking,
indicates how the fertilizer tax has shifted the financial return
positions of the various fanning options. The CBWM options, those using
the least amount of nitrogen fertilizer, have moved up in the ranking for
the upland soils. The ranking of the continuous corn, no-tillage options,
146
-------
Table A-7D. Fertilizer Costs — Fertilizer Tax Alternative
Itom
Tillage Practices
C Cony, C Chisel C No-till
Rotations
CB Cony,
CB Chisel
CBNN
CB No-till Part. No-till
CBMH
NQ-tiU. Herb,
Terraces
CConv.
C Chisel
CB No-till
Average Annual Fertilizer
amount, Ibs/acre*
N
A uplands
B ridge
C lowlands
PjOS
A uplands
B ridge
C lowlands
KjO
Cost of Fertilizer
per acre, $••
A uplands
B ridge
C lowlands
125
160
160
44
40
40
50
37.86
44.10
44.10
125
160
160
44
40
40
50
37.86
44.10
44.10
137.50
176
176
44
40
40
50
40.36
47.30
47.30
57.50
75
75
27.50
25
25
60
22.13
25.15
25.15
57.50
75
75
27.50
25
25
60
22.13
25.15
25.15
63.25
82.50
82.50
27.50
25
25
60
23.28
26.65
26.65
33.75
42.50
42.50
24.75
22.50
22.50
70
17.75
19.08
19.08
35.63
45.25
45.25
24.75
22.50
22.50
70
18.13
19.63
19.63
125
160
160
44
40
40
50
37.86
44.10
44.10
125
160
160
44
40
40
50
37.86
44.10
44.10
63.25
82.50
82.50
27.50
25
25
60
23.28
26.65
26.65
Total Cost of Ferti-
lizer, $
A uplands
B ridge
C lowlands
9,465
11,025
11,025
Rental of Application
Equipment, 5* 262.50
Total Fertilizer
Costs, $
A uplands
B ridge
C lowlands
Notes: C » corn;
9,727.50
11,287.50
11,287.50
9,465
11,025
11,025
262 . 50
9,727.50
11,287.50
11,287.50
CB m corn-bean; CBHM
10,090
11,825
11,825
262 . 50
10,352.50
12,087.50
12,087.50
5,532.50 5,
6,287.50 6,
6,287.50 6,
131.25
5,663.75 5,
6,237.50 6,
6,237.50 6,
532.50
287.50
287.50
131.25
663.75
237. 5C
237.50
5,820
6,662.50
6,662.50
131.25
5,951.25
6,793.75
6,793.75
4,437.50
4,770
4,770
153.13
4,590.63
4,923.13
4,932.13
4,532.50
4,907.50
4,907.50
153.13
4,685.63
5 , 060 . 63
5,060.63
9,465
11,025
11,025
262 . 50
9,727.50
11,287.50
11,287.50
9,465
11,025
11,025
262 . 50
9,727.50
11,287.50
11,287.50
5,820
6,662.50
6,662.50
131.25
5,951.25
6,793.75
6,793.75
= corn-bean-wheat-meadow.
Derivation shown in Table A-7, See footnotes Table A-7.
Assume prices per Ib. are SO.20 for N ($.07 tax), SO.19 for PzOs, and SO.09 for KzO.
-------
Table A-10D.
Other Costs ~ Fertilizer Tax Alternative
00
XteB
Corn Drying
Total Cost*
A uplands
B ridge
C lowlands
Capital*
Fertiliser (8 mo.)
A uplands
B ridge
C lowlands
A uplands
B ridge
C lowlands
A uplands
B ridge
C lowlands
Total Interest
A uplands
B ridge
C uplands
Total Other Costs
A uplands
B ridge
C lowlands
Tillage Practices
C Conv. c Chisel
4,200 4,200
5,200 5,200
5,200 5,200
k*
744.15 744.15
863.49 863.49
863.49 863.49
134.87 134.87
148.47 148.47
162.07 162.07
242.46 242.46
222.06 222. O6
262.97 262.97
30.32 28.40
30.88 27.78
1,182.68 1,177.66
1,295.22 1,290.20
1,350.73 1,345.71
5,382.68 5,377.66
6,495.22 6,490.20
6,550.73 6,545.71
Motes; C - cornj CB - corn -bean; CBHM -
* Derivation shown in Table A-10,
** Fertilizer costs frost Table A-7D.
C No-till
3,990
5,200
4,160
791.97
924.69
924.69
141.67
155.27
168.87
479.61
452.09
507.03
23.82
20.37
1,457.44
1,576.24
1,644.78
5,447.44
6,776.24
5,8O4.78
2,205 2
2,730 2
2,730 2
433.28
484.32
484 . 32
143.93
150.73
157.53
192.95
165.22
219.88
22.82
21.81
814.79
844.90
912.36
3,019.79 3,
3,574.90 3,
3,642.36 3,
Chisel
,205
,730
,730
433.28
484.32
484.32
143.93
150.73
157.53
192.95
165.22
219.88
23.67
23.17
817
847.11
914.57
022
577.11
644.57
Hotati
CB No-till
2,205
2,730
2,457
455.27
519.72
519.72
151.16
157.96
164.76
257.66
227.38
287.25
19.08
15.77
898 . 94
939.91
3,103.94
3,669.91
3,463.58
corn-bean-wheat -Meadow .
see footnotes Table A-10 .
ons
CBMN
Part. No-till
1,155
1,430
1,430
351.18
376.62
376.62
163.34
166.74
170.14
121.13
1O6.83
134.99
32.05
17.12
684.82
699.36
730.92
1,839.82
2,129.36
2,160.92
CBWM
No-till. Herb.
1,155
1,430
358.45
387 . 14
160.22
163.62
148.35
134.06
30.29
14.25
711.55
729.35
760.91
1,866.55
2,159.35
2,119.41
C Conv.
4,480
5,480
744.15
863.49
134.87
148.47
242.46
222.06
30.32
30.88
1,182.68
1,295.22
5,662.68
6,775.22
6,830.73
C Chisel
4,430
5,480
744.15
863.49
134.87
148.47
242.46
222.06
28.40
27.78
1,177.66
1,290.20
5,657.66
6,770.20
6,825.71
1
2,345
2,870
455.27
591 . 72
151.16
157.96
257.66
227.38
19 08
15.77
898.94
939.91
3,243.99
3,809.91
3,603 58
-------
Table A-12D. S
y — Fertilizer Tax Alternative
Item
Gross Revenue, $
C Conv
A uplands 52,500
B ridge 65,000
C lowlands 65,000
Tillage Practices
C Chisel
52,500
65,000
65,000
C No-till
Rotations
CB Conv.
49,875 46,312.50
65,000 59,125
52,000 59,125
CB Chisel
46,312.50
59,125
59,125
CB No-till
44,437.50
57,875
50,712.50
CBNM
Part. No-till
43,031.25
51,781.25
49,906.25
CBHN
No-till, Herb.
Terraces
C Conv.
43,031.25 56,000
51,781.25 68,500
49,012.50 68,500
C Chisel
56,000
68,500
68 , 500
CB No-till
47,437.50
60,875
53,712.50
ID
Costs
Tractor (excl.
fuel)
Inplenents
(excl. fuel)
Fuel
Seed
A uplands
B ridge
C lowlands
Fertilizer*
A uplands
B ridge
C lowlands
Pesticides
A uplands
B ridge
C lowlands
Labor
Terracing
Other**
A uplands
B ridge
C lowlands
Total Cost (Net of
Land Cost)
A uplands
B ridge
C lowlands
Net Return (Excl.
Land Cost)
A uplands
B ridge
C lowlands
Notes: C » corn;
4,604.91
10,643.65
1,426.99
2,380
2,620
2,860
9,727.50
11,287.50
11,287.50
5,705
5,225
6,187.50
2,149.42
0
5,382.68
6,495.22
6,550.73
42,020.15
44,452.69
45,710.70
10,479.85
20,547.31
19,289.30
4,537.42
10,365.66
1,336.54
2,380
2,620
2,860
9,727.50
11,287.50
11,287.50
5,705
5,225
6,187.50
2,019.30
0
5,377.66
6,490.20
6,545.71
41,449.40
43,881.62
45,139.63
11,050.60
21,118.38
19,860.37
CB - corn-bean; CBHM
4,281.19
8,913.07
1,120.86
2,500
2,740
2,980
10,352.50
12,087.50
12,087.50
11,285
10,637.50
11,930
1,708.98
0
5,447.44
6,776.24
5,804.78
45,609.04
48,264.84
48,826.38
4,265.96
16,735.16
3,173.62
4,272.26
11,134.31
1,073.97
2,540
2,660
2,780
5,663.75
6,237.50
6,237.50
4,540
3,887.50
5,173.75
1,691.76
0
3,019.79
3,574.90
3,642.36
33,935.84
37,531.95
36,005.91
12,376.66
21,593.02
23,119.09
4,301.95
10,828.87
1,113.78
2,540
2,660
2,780
5,663.75
6,237.50
6,237.50
4,540
3,887.50
5,173.75
1,671.68
0
3,022
3,577.11
3,644.57
33,682.03
34,278.39
35,752.10
12,630.47
24,846.61
23,372.90
4,056.64
9,376.28
898.10
2,667.50
2, 787. SO
2 , 907 . 50
5,951.25
6,793.75
6,793.75
6,062.50
5,350
6,758.75
1,361.36
0
3,103.94
3,669.91
3,463.58
33,477.57
34,293.54
35,615.96
10,959.93
23,581.46
15,096.54
4.734.7X
14,493.38
1.508.40
2,882.50
2,942.50
3,002.50
4,590.63
4,923.13
4,923.13
2,850.01
2,513.76
3,176.25
2,215.42
0
1,839.82
2,129.36
2,160.92
35,114.87
35,496.66
36,214.73
7,916.38
16,284.59
13,691.52
4,672.41
13,728.36
1.429.91
2,912.50
2,972.50
3,032.50
4,685.63
5,060.63
5,060.63
3,490.63
3,154.38
3,816.88
2,095.30
0
1,866.55
2,159.35
2,119.41
34,881.29
35,272.84
35,955.40
8,149.96
16,508.41
13,057.10
4,604.91
10,643.65
1,426.99
2,380
2,620
2,860
9,727.50
11,287.50
11,287.50
5,705
5,225
6,187.50
2,149.42
6,460
5,662.68
6,775.22
fc,830.73
48,760.15
51,192.69
52,450.70
7,239.85
17,307.31
16,049.30
- corn-bean-wheat-meadow.
4,537.42
10,365.66
1,336.54
2,380
2,620
2,860
9,727.50
11,287.50
11,287.50
5,705
5,225
6,187.50
2,019.30
6,460
5,657.66
6,770.20
6,825.71
48,189.08
50,621.62
51,879.63
7,810.92
17,878.38
16,620.37
4,056.64
9,376.28
898.10
2,667.50
2,787.50
2,907.50
5,951.25
6,793.75
6,793.75
6,062.50
5,350
6,758.75
1,361.36
6,460
3,243.94
3,809.91
3,603.58
40.077.57
40,943.54
42,215.96
7,359.93
19,931.46
11,496.54
Fertilizer costs from Table A-7D, **Other costs from Table A-10D.
-------
which use the most nitrogen, is not affected on any of the soil types since
the net return for these options was so low in the base case. The rankings
of the options with the highest net returns, corn-soybean rotation and
continuous corn using chisel and conventional tillage, are not greatly
affected by the fertilizer tax even though these options are heavy users
of nitrogen. The level of revenue returned to these options is lowered
slightly, however. Overall, it can be concluded that not much change has
been affected by the tax.
What is found from this comparison is that, in general, nitrogen
fertilizer costs are not that great relative to other expenses which the
farmer incurs, and therefore a nitrogen fertilizer tax, unless it is
extremely large, will not affect net revenue enough to cause a fanner to
switch farming practices. Fertilizer costs range from about 12 percent
to 20 percent of the total costs that have been calculated for the
farming practice options considered. Nitrogen costs make up 30 to 65
percent of total fertilizer costs, depending on the option considered.
Since nitrogen fertilizer costs are so small a factor, a tax such as the
one considered here will not have a significant impact. If the tax were
imposed after an energy cost increase had occurred, however, such as that
considered in Alternative B, then a greater impact might be observed.
Unfortunately, the example case is not flexible enough as it stands
to account for the most realistic farmer response to a tax such as the
one considered in Alternative D. Rather than switch tillage or rotation
options' in response to net revenue charges, as hypothesized here, a
farmer most probably would change his method of nitrogen fertilizer
150
-------
application to increase the use of nitrogen as a side dressing. This
response would tend to decrease the amount of nitrogen used while main-
taining generally the same rotation and tillage practices.
Table A-13D. Net Revenue Ranking—Fertiliser Tax Alternative
Uplands
high CB Chisel
CB Conv.
C Chisel
CB No-till
C Conv.
CBWM-Herb .
CBWM-Part.
C Chisel-Ter.
CB No-t.-Ter.
C Conv.-Ter.
low C No-till
Ridge Lowlands
CB Chisel CB Chisel r
CB No-till CB Conv. 1
CB Conv. C Chisel 1
C Chisel C Conv. r
C Conv. C Chisel-Ter. r
CB No-t.-Ter. C Conv.-Ter. r
C Chisel-Ter. CB No-till r
C Conv.-Ter. CBWM-Part. r
C No-till CBWM-Herb. 1
CBWM-Herb. CB No-t.-Ter. 1
CBWM-Part. C No-till r
r
r
1
r
r
1
1
1
1
u
u
1
u
u
u
u
u
u
u
u
u
1
All Soils
CB Chisel
CB Chisel
CB Conv.
CB No- till
CB Conv.
C Chisel
C Conv.
CB No-t.-Ter.
C Chisel
C Conv.
C Chisel-Ter.
C Conv.-Ter.
C No-till
C Chisel-Ter.
CBWM-Herb.
CBWM-Part.
C Conv.-Ter
CB No-till
CBWM-Part .
CBWM-Herb.
CB Chisel
CB Conv.
CB No-t.-Ter.
C Chisel
CB no-till
C Conv.
CBWM-Herb .
CBWM-Part .
C Chisel-Ter.
CB No-t.-Ter.
C Conv.-Ter.
C No-till
C No-till
•*- 26,000
«- 24,000
«- 20,000
•*- 19,000
<- 17,000
•*• 13,000
•*- 11,000
•*• 8,000
•*- 7,000
«- 3,000
Notes; C = corn; CB = corn-bean; CBWM = corn-bean-wheat-meadow.
r = ridge; 1 = lowlands; u = uplands.
151
-------
Alternative E: Insecticide Scouting
This alternative is based on the premise that the amount of insecti-
cides used on corn can be reduced by scouting to determine areas with
high potential soil insect problems and by treating only those fields that
need treatment with the full recommended dosage. Other areas would not
be treated for these pests. Alternative E shows the effects on net
revenue of such a reduced pesticide program on a typical farm in the
case study area.
Table A-8E gives pesticide costs under the scouting alternative.
Insecticide costs per acre for corn are determined in the same manner
as for Table A-8. The number of acres treated are based on approximate
percentages (listed in the footnote to Table A-8E) that might apply to a
typical farm on the soils and for the crop rotations under consideration.
Scouting costs are based on an assumed $2.00 per acre cost for the number
of acres that would typically need scouting for the soil types being
considered. The lowlands, for example, are wetter and therefore more
likely to harbor certain insects. Herbicides applied to corn are not
affected by the scouting option, nor are soybean pesticide costs since
no insecticides were applied to soybeans in the base case. The compari-
son of "Total Pesticide Cost" in Table 8E with that in Table A-8 shows that
the scouting option has reduced pesticide costs by anywhere from $800 to
$2,250 and 12 to 40 percent depending on the farming practice used.
Table A-10E shows slightly reduced interest costs compared to Table A-10
in response to the reduced pesticide costs under the scouting alternative.
The reduced pesticide and interest costs are summarized in Table A-12E along
with other costs which are the same as for the base case. Note that
152
-------
Table A-8E. Pesticide Costs — Insectivide Scouting Alternative
cn
Item
Tillage Practices
C Conv. C Chisel C No-till
Rotations
CBWM
CB Conv. CB Chisel CB No-till Part. No-till
CBWM
No-till, Herb.
Terraces
C Conv. C Chisel
CD NO-tlll
Corn, Cost
Herbicide, S/acre*
A uplands
B ridge
C lowlands
Acres
Herbicide Cost, $
A uplands
B ridge
C lowlands
Insecticide , $/acre*
Acres treated**
A uplands
B ridge
C lowlands
Insecticide cost, $
A uplands
B ridge
C lowlands
Scouting Cost/acre,
Acres Scouted
A uplands
B ridge
C lowlands
Total Scouting Cost,
A uplands
B ridge
C lowlands
Total Cost, $
A uplands
B ridge
C lowlands
Soybeans, Cost
Total Cost, $*
A uplands
B ridge
C lowlands
Total Pesticide
Cost, $
A uplands
B ridge
C lowlands
13.51
11.59
15.44
250
3,377.50
2,897.50
3,860
9.31
100
100
100
931
931
931
$ 2.00
250
250
250
$
500
500
500
4,808.50
4,328.50
5,291
4,808.50
4,328.50
5,291
13.51
11.59
15.44
250
3,377.50
2,897.50
3,860
9.31
100
100
100
931
931
931
2.00
250
250
250
500
500
500
4,808.50
4,328.50
5,291
4,808.50
4,328.50
5,291
Notes: C - corn; CB - corn-bean; CBWM =
26.78
24.19
29.36
250
6,695
6,047.50
7,340
18.36
100
100
100
1,836
1,836
1,836
2.00
250
250
250
500
500
500
9,031
8,383.50
9,676
9,031
8,383.50
9,676
13.51
11.59
15.44
125
1,688.75
1,448.75
1,930
9.05
1.25
1.25
6.25
11.31
11.31
56.56
2.00
25
25
125
50
50
250
1,750.06
1,510.06
2,236.56
1,720
1,307.50
2,112.50
3,470.06
2,817.56
4,349.06
13.51
11.59
15.44
125
1,688.75
1,448.75
1,930
9.05
1.25
1.25
6.25
11.31
11.31
56.56
2.00
25
25
125
50
50
250
1,750.06
1,510.06
2,236.56
1,720
1 , 307 . 50
2,112.50
3,470.06
2,817.56
4,349.06
24.50
22.26
26.78
125
3 , 062 . 50
2,782.50
3,347.50
9.05
1.25
1.25
6.25
11.31
11.31
56.56
2.00
25
25
125
50
50
250
3,123.81
2,843.81
3,654.06
1,868.75
1,436.25
2,280
4,992.56
4,280.06
5,934.06
13.51
11.59
15.44
62.5
844 . 38
724.38
965
17.14
0.63
0.63
4.69
10.80
10.80
80.39
2.00
12.50
12.50
31.25
25
25
62.50
880.18
760.18
1,107.89
934.38
718.13
1,140
1,814.56
1,478.31
2,247.89
23.76
21.84
25.69
62.5
1,485
1,365
1,605.63
17.14
0.63
0.63
4.69
10.80
10.80
80.39
2.00
12.50
12.50
31.25
25
25
62.50
1,520.80
1,400.80
1.748.52
934.38
718.13
1,140
2,455.18
2,118.93
2,888.52
13.51
11.59
15.44
250
3,377.50
2,897.50
3,860
9.31
100
100
100
931
931
931
2.00
250
250
250
500
500
500
4,808.50
4,328.50
5,291
4,808.50
4,328.50
5,291
13.51
11.59
15.44
250
3,377.50
2,897.50
3,860
9.31
100
100
100
931
931
931
2.00
250
250
250
500
500
500
4,808.50
4,328.50
5,291
4,808.50
4,328.50
5,291
24.50
22.26
26.78
125
3,062.50
2,782.50
3,347.50
9.05
1.25
1.25
6.25
11.31
11.31
56.56
2.00
25
25
125
50
SO
250
3,123.81
2,843.81
3,654.06
1,868.75
1,436.25
2,280
4,992.56
4,280.06
5,934.06
corn-bean-wheat-meadow .
see Table A-8 for derivation; see .footnotes, Table A-8.
** Assumes 40% continuous corn treated; 7.5% lowlands CB and CBWM treated; 1% uplands, ridge CB and CBWM treated, based upon discussions
with Dr. Thomas Turpin, Purdue University and on Turpin, F. T., "Insect Insurance: -Potential Management Tool for Corn Insects," in
Bulletin of the Entomological Society of America, Vol. 23, No. 3, pp. 181-184, September 1977.
-------
Table A-10E.
Ul
Other Costs — Insecticide Scouting Alternative
Item
Tillage Practices
C Conv.
Corn Drying
Total Cost*
A uplands 4,200
B ridge 5,200
C lowlands 5,200
Capital*
Fertilizer (8 no.)
A uplands 576.81
B ridge 649.29
C lowlands 649.29
A uplands
B ridge
C lowlands
A uplands
B ridge
C lowlands
A uplands
B ridge
C lowlands
Total Other Costs
A uplands
B ridge
C lowlands
134.87
148.47
162 . 07
204 . 36
183.96
224.87
977.24
1,042.92
1,098.43
5,177.24
6,242.92
6,298.53
C Chisel
4,200
5,200
5,200
576.81
649.29
649.29
134.87
148.47
162.07
204.36
183.96
224.87
972.22
1,037.90
1,093.41
5,172.22
6,237.91
6,293.41
C No-till
3,990
5,200
4,160
607.98
689.07
689. O7
141.67
155.27
168.87
383.82
356.30
411.23
23.82
1,177.66
1,244.83
1,313.36
5,167.66
6,444.83
5,473.36
. Rotations
CB Conv.
2,205
2,730
2,730
356.39
390.63
390.63
143.93
150.73
157.53
147.48
119.74
184.84
22.82
21.81
692.43
705.73
777.63
2,897.43
3,435.73
3,507.63
CB Chisel
2,205
2,730
2,730
356.39
390.63
390.63
143.93
150.73
157.53
147.48
119.74
184.84
23.67
23.17
694.64
707.94
779.84
2,899.64
3,437.94
3,509.84
CB No-till Pa
2,205
2,730
2,457
370.55
409.37
409.37
151.16
157.96
164.76
212.18
181.90
252.20
15.77
768.74
784.06
861 . 18
2,973.73
3,514.08
3,318.18
CBWH
1,155
1,430
1,430
306.05
319.82
319.82
163.34
166.74
77.12
62.83
595.68
598 . 56
1,750.68
2,028.56
2,064.67
CBWH
Terraces
C Conv. C Chisel CR Nn-t-i'l
1,155 4,480
1,430 5,480
1,358.50 5.480
310.64
326.51
160.22
163.62
104.35
90.05
576.81
649.29
134.87
148.47
204.36
183.96
619.74 977.24
624.71 1,042.92
1,774.74
2,054.71
2,019.32
5,457.24
6,522.92
6,578.43
4,480
5,480
5,480
576.81
649.29
134.87
148.47
204.36
183.96
972.22
1,037.90
5,452.22
6,517.90
6,573.41
2,345
2,870
2 597
370.55
409.37
151.16
157.96
212.18
181.90
768.74
784.08
3,113.74
3,654.08
3,458.18
ttoteg! c • corn-beanj CB • corn-bean; CBWM - corn-bean-wheat-meadow.
'Derivation shown in Table A-10. See footnotes Table A-10, "Pesticide costs from Table A-8E.
-------
Table A-12E. Stannary — Insecticide Scouting Alternative
Item
Tillage Practices
C Conv. C Chisel C No-till
Rotations
CBHM
CB Conv. CB Chisel CB No-till Part. No-till
CBHM
Ko-till, Herb.
Terraces
C Conv. C Chisel
CB Mo-till
Ln
Gross Revenue, $
A uplands
B ridge
C lowlands
Costs
Tractor (excl.
fuel)
Implements
(excl. fuel)
Fuel
Seed
A uplands
B ridge
C lowlands
Fertilizer
A uplands
B ridge
C lowlands
Pesticides*
A uplands
B ridge
C lowlands
Labor
Terracing
Other**
A uplands
B ridge
C lowlands
52,500
65,000
65,000
4,604.91
10,643.65
1,426.99
2.380
2,620
2,860
7,540
8,487.50
8,487.50
4,808.50
4,328.50
5,291
2,149.42
0
5,177.24
6,242.92
6,298.53
Total Cost iNet of
Land Cost)
A uplands 38,790.71
B ridge 40,503.89
C lowlands 41,762
Net Return (Excl.
Land Cost)
A uplands
B ridge
C lowlands
Notes: C = corn;
13,769.29
24,496.11
23,238
52,500
65,000
65,000
4,537.42
10,365.66
1,336.54
2,380
2,620
2,860
7,540
8,487.50
8,487.50
4,808.50
4,328.50
5,291
2,019.30
0
5,172.22
6,237.91
6,293.41
38,159.64
39,933.05
41,190.83
14,340.36
25,067.17
23,809.17
CB • corn-bean; CBWM
49,875
65,000
52,000
4,281.19
8,913.07
1,120.86
2,500
2,740
2,980
7,947.50
9 , 007 . 50
9,007.50
9,031
8,383.50
9,676
1,708.98
0
5,167.66
6,444.83
5,473.36
40,670.26
42,599.43
43,160.97
9,204.74
22,400.57
8,839.03
46,312.50
59,125
59,125
4,272.26
11,134.31
1,073.97
2,540
2,660
2,780
4,658.75
5,106.25
5,106.25
3,470.06
2,817.56
4,349.06
1,691.76
0
2,897.43
3,435.73
3,507.63
31,738.54
32,191.59
33,915.24
14,573.96
26,933.41
25,209.76
46,312.50
59,125
59,125
4,301.95
10,828.87
1,113.78
2,540
2,660
2,780
4,658.75
5,106.25
5,106.25
3,470.06
2,817.56
4,349.06
1,671.68
0
2,899.64
3,437.94
3,509.84
31,484.73
31,938.03
33,661.43
14,827.77
27,186.97
25,463.57
44,437.50
57,875
50,712.50
4,056.64
9,376.28
898.10
2,667.50
2,787.50
2,907.50
4,843.75
5,351.25
5,351.25
4,992.56
4,280.06
5,934.06
1,361.36
0
2,973.73
3,514.08
3,318.18
31,169.92
31,625.27
33,203.37
13,267.58
26,249.73
17,509.13
43,031.25
51,781.25
49,906.25
4,734.71
14,493.38
1.508.40
2,882.50
2,942.50
3.OO2.50
4,OO0.63
4,180.63
4,180.63
1,814.56
1,478.31
2,247.89
2,215.42
0
1,750.68
2,028.56
2,064.67
33,400.28
33,581.91
34,447.62
9,630.97
18,199.34
15,458.63
43,031.25
51,781.25
49,012.50
4,672.41
13,728.36
1.429.91
2,912.50
2,972.50
3,032.50
4,060.63
4,268.13
4,abH.13
2,455.18
2,118.93
2,888.52
2,095.30
0
1,774.74
2,054.71
2,019.32
33,129.03
33,340.25
34,134.45
9,902.22
18,441.00
14,873.05
56,000
68 , 500
68,500
4,604.91
10,643.65
1,426.99
2,380
2,620
2,860
7,540
8,487.50
8,487.50
4,808.50
4,308.50
5,291
2,149.42
6,460
5,457.24
6,522.92
6,578.43
45,470.89
47,243.89
48,501.90
10,529.11
21,256.11
19,998.10
56,000
68 , 500
68,500
4,537.42
10,365.66
1,336.54
2,380
2,620
2,860
7,540
8,487.50
8. 487. 50
4,808.50
4,328.50
5,291
2,019.30
6,460
5,452.22
6,517.90
6,573.41
44,899.64
46,672.82
47,930.83
11,100.36
21,827.18
20,569.17
47,4J7.-J.'1
60,875
53,712.30
4,056.64
9,376.23
893.10
2,667. 50
2,787.50
2,907.50
4,843.75
5,351.25
5.351.25
4,992.56
4,280.06
5,934.06
1,361.36
6,460
3,113.74
3,654.08
3,458.18
37,769.87
38,225.27
39,803.37
9,667.63
22,649.73
13,909.13
= corn-bean-wheat-meadow.
Pesticide costs from Table A-8E, **Other COStS from Table A-10E.
-------
gross revenue in Table A-12E is the same as in. Table A-12; the scouting and
selected treatment with the recommended insecticide dosage has insured
that there is no yield loss under this alternative. Net returns have
been increased slightly, approximately $1,000 for all options except the
continuous corn no-tillage options for which revenue increased by $2,350.
Table A-13E shows the net revenue ranking of the farming practice
options under the scouting alternative. When compared with Table A-13,
it can be seen that the revenue changes caused by reducing insecticide
use through scouting are not significant enough to cause many changes
in ranking of the options. When each soil type is considered separately
the only ranking change which occurs is the movement of the continuous
corn no-tillage option from ninth to seventh place on the ridge soils.
When all soils are considered together the only change is that net
revenue increases slightly and the continuous corn no-tillage option on the
ridge soil moves up two places. The revenue for the two continuous corn no-
tillage options on the uplands and lowlands has been significantly increased as
shown by the lower net revenue bound change from $6,000 in Table A-13 to
$8,000 in Table A-13E. The relative net return of these two options is
so low in the base case, however, that their ranking is not affected by
the revenue increase. The three continuous corn no-tillage options are
most affected by the pesticide scouting alternative because in the base
case they require the most insecticide; for the other options, insecticide
costs are not high enough relative to other production inputs for finan-
cial returns to be significantly altered by their reduction. Pesticide
costs account for 8 to 26 percent of total costs, depending on the
farming practice used and insecticide costs are 30 to 40 percent of
156
-------
pesticide costs. More interesting results might be gained by applying
the scouting option to the increased energy cost scenario where it might
serve to reduce a very expensive input.
Table A-13E.
Uplands
Net Revenue Ranking—Insecticide Scouting Alternative
Ridge Lowlands All Soils
high CB Chisel CB Chisel CB Chisel r
CB Conv. CB Conv. CB Conv. r
C Chisel CB No-till C Chisel r
C Conv. C Chisel C Conv. 1
CB No-till C Conv. C Chisel-Ter. 1
C Chisel-Ter. CB No-t.-Ter. C Conv.-Ter. r
C Conv.-Ter. C No-till CB No-till r
CBWM-Herb. C Chisel-Ter. CBWM-Part. 1
CB No-t.-Ter. C Conv.-Ter. CBWM-Herb. 1
CBWM-Part. CBWM-Herb. CB No-t.-Ter. r
low C No-till CBWM-Part C No-till r
r
r
r
1
1
r
r
1
1
1
u
u
u
1
u
u
u
u
u
u
u
u
1
CB Chisel
CB Conv. ^_
CB No-till
CB Chisel
CB Conv.
C Chisel
C Conv.
-«-
C Chisel
C Conv.
CB No-till-Ter.
C No-till ^
C Chisel Ter.
C Conv.-Ter.
C Conv.-Ter.
C Chisel-Ter.
C Conv.-Ter.
CBWM-Herb.
CBWM-Part.
CB No-till
•«-
CBWM-Part .
CBWM-Herb.
CB Chisel
CB Conv.
C Chisel
+•
CB No-till-Ter.
C Conv.
CB No-till
•«-
C Chisel-Ter.
C Conv.-Ter. ^_
CBWM-Herb.
C CB No-till-Ter.
CBWM-Part . ,
•<-
C No-till
C No-till
•«-
28,000
26,000
24,000
22,000
20,000
17 ,000
14 ,000
13 ,000
10,000
9,000
8,000
Notes; C = corn; CB = corn-bean; CBWM = corn-bean-wheat-meadow.
r = ridge; 1 = lowlands; u = uplands.
157
-------
Alternative F: No Insecticide Treatment
This alternative is the extreme end of the variation examined in
Alternative E. In this case no insecticide treatments are used for
any of the options. Table A-8F shows that pesticide costs have been
reduced by the elimination of insecticide costs; only herbicide costs
remain. Total pesticide costs have been decreased by approximately
$1,000 to $4,500 or by 30 to 45 percent depending on the farming prac-
tice (compare with Table A-8) .
In Table A-10F these reduced pesticide costs are translated into
correspondingly reduced interest costs. Corn drying costs are also
reduced since yield loss occurs as a result of insect damage. Table
A-11F shows the change in yield due to this loss caused by lack of insecti-
cide treatment. Losses differ according to soil types and crop rotations
used and are detailed in a footnote to Table A-11F. Crop loss, of course,
reduces gross revenue. Comparing Table A-11F to Table A-ll, it can be seen
that gross revenue is reduced significantly for the continuous corn
options ($2,000) but only slightly for the other options ($5 to $50).
Table A-12F summarizes the effects of reduced gross revenue and
reduced pesticide costs. Net returns for all options have been increased
\
slightly compared to the base case (Table A-12): about $600 for the continu-
ous corn chisel and conventionally tilled options; approximately $3,000
for the continuous corn no-tillage options; and about $1,000 for all other
options.
The net revenue ranking of all options under this alternative is
shown in Table A-13F. As was true for Alternative E, there are 'relatively
158
-------
Table A-8P. Pesticide Costs — No Insecticide Treatment Alternative
Item
Tillage Practices
C Conv. C Chisel C No-till
Rotations
CBWM
CB Conv. CB Chisel CB No-till Part. No-till
CBWM
No-till, Herb.
Terraces
C Conv. C Chisel Cb :;o-til
Ln
Corn , Cost
Herbicide, $/acre
A uplands
B ridge
C lowlands
Acres
Total Cost, $
A uplands
B ridge
C lowlands
Soybeans, Cost
Total Cost, $*
A uplands
B ridge
C lowlands
Total Pesticide
Cost, $
A uplands
B ridge
C lowlands
Notes: C - corn;
*
13.51
11.59
15.44
250
3,377.50
2,897.50
3,860
3,377.50
2,897.50
3,860
13.51
11.59
15.44
250
3,377.50
2,897.50
3,860
3,377.50
2,897.50
3,860
CB - corn-bean; CBWM =
26.78
24.19
29.36
250
6,695
6,047.50
7,340
6,695
6,047.50
7,340
13.51
11.59
15.44
125
1,688.75 1
1,448.75 1
1,930 1
1,720 1
1,307.50 1
2,112.50 2
3,408.75 3
2,756.25 2
4,042.50 4
13.51
11.59
15.44
125
,688.75
,448.75
,930
,720
,307.50
,112.50
,408.75
,756.25
,042.50
24.50
22.26
26.78
125
3,062.50
2,782.50
3,347.50
1,868.75
1,436.25
2,280
4,931.25
4,218.75
5,627.50
13.51
11.59
15.44
62.5
844.38
724.38
965
934.38
718.13
1,140
1,778.76
1,442.51
2,105
23.76
21.84
25.69
62.5
1,485
1,365
1,650.63
934.38
718.13
1,140
2,419.33
2,083.13
2,745.64
13.51
11.59
15.44
250
3,377.50
2 , 897 . 50
3,860
3,377.50
2,307.50
3,860
13.51
11.59
15.44
250
3,377.50
2,897.50
3,860
3,377.50
2,897.50
3,860
24.50
22.26
26.78
125
3,062.50
2,782.50
3,347.50
1,868.75
1,436.25
2,280
4,931.25
4,218.75
5,627.50
corn-bean-wheat-meadow .
* See Table A-8 for derivation; see footnotes Table A-8.
-------
Table A-10P. other Costs - No Insecticide Treatment Alternative
cr>
o
Item
Corn Drying
Tillage Practices
C Conv.
Grain harvested, bu. *
A uplands 25.250
B ridge 31,500
C lowlands 31,500
Total Cost
A uplands
B ridge
C lowlands
4,040
5,040
5,040
Interest on Operating
Capital**
Fertilizer (8 so.)
A uplands 576.81
B ridge 649.29
C lowlands 649.29
A uplands
B ridge
C lowlands
Pesticide (6 mo.)*
A uplands
B ridge
C lowlands
Fuel (3 so.)
Labor (3 mo.)
Total Interest
A uplands
B ridge
C lowlands
Total Other Costs
A uplands
B ridge
C lowlands
134.87
148.47
162.07
143.54
123.14
164.05
30.32
30.88
916.42
982.10
1,081.02
4,956.42
6,022.10
6,077.61
C Chisel
25,250
31,500
31,500
4,040
5,040
5,040
576.81
649.29
649.29
134.87
148.47
162.07
143.54
123.14
164.05
28.40
27.78
911.40
977.08
1,032.59
4,951.40
6,017.08
6,072.59
C No-till
Rotations
CB Conv. CB
23,937.50 13,775.62
31,50O 17,056.87
25,000 17,034.37
3,830 2,204.10
5,020 2,729.10
4,000 2,725.50
607.98 356.39
689.07 390.63
689.07 390.63
141.67 143.93
155.27 150.73
168.87 157.53
284.54 144.87
257.02 117.14
311.95 171.81
23.82 22.82
20.37 21.81
1,078.38 689.82
1,145.55 703.13
1,214.08 764.60
4,908.38 2,893.92
6,185.55 3,432.23
5,214.08 3,490.10
Chisel CB
13,775.62
17,056.87
17,034.37
2,204.10
2,729.10
2,725.50
356.39
390.63
390.63
143.93
150.73
157.73
144.87
117.14
171.81
23.67
23.17
692. O3
705.34
766.81
2,896.13
3,434.44
3,492.31
CBWM
No-till Part. No-till
13,775.62
17,056.87
15,328.12
2,204.10
2,729.10
2,452.50
370.55
409.37
409.37
151.16
157.96
164.76
209.58
179.30
239.17
19.08
15.77
766.14
781.48
848,15
2,970.24
3,510.58
3.3O0.65
7,215.94
8,934.69
8,916.41
1,154.55
1,429.55
1,426.63
306 . 05
319.82
319.82
163.34
166.74
170.14
75.60
61.31
89.46
32. 05
17.12
594.16
597.04
628.59
1,748.71
2,026.59
2,055.22
CBWM
No-till, Herb.
Terraces
C Conv.
7,215.94 27,000
8,934.69 33,250
8,469.54 33,250
0.16
1,154.55
1,429.55
1,355.13
310.64
326.51
326.51
160.22
163.62
167.02
102.82
88.53
116.69
30.28
14.25
618.21
623.19
654.75
1,772.76
2,052.74
2,009.88
O.16
4,320
5,320
5,320
576.81
649.29
649.29
134.87
148.47
162 . 07
143 . 54
123.14
164.05
30.32
30.88
916.42
982.10
1,037.61
5,236.42
6,302.10
6,357.61
C Chisel
27,000
33,250
33,250
0.16
4,320
5,320
5,320
576.81
649.29
649.29
134.87
148.47
162.07
143.54
123.14
164.05
28.40
27.78
911.40
977.08
1,032.59
5,231.40
6,297.08
6,352.59
CB No-till
14,650.62
17,931.87
16,203.12
0.16
2,344.10
2,869.10
2,592.50
370.55
409.37
409.37
151 . 16
157.96
164.76
209.58
179.30
239.17
19.08
15.77
766.14
781.48
848.15
3,110.24
3,650.58
3,440.65
corn; CB - corn-bean; CBWM - corn-bean-wheat-Beadov.
From Table A-11F, **See footnotes Table A-10, ***From Table A-8F.
-------
Table A-11F. Revenue — No Insecticide Treatment Alternative
I tea
Tillage Pra
C Conv. C Chisel
c'ices
C No-till
Rotations
CB Conv. CB Chisel CB No-till
CBWM
Part. No-till
CBWM
No-till, Herb.
Terraces
C Conv. C Chisel CB No-till
Corn
Expected yield.
bu/acre **
A uplands 105
B ridge 130
C lowlands 130
Loss*
A uplands 1,000
B ridge 1,000
Total output, bu.
A uplands 25,250
B ridge 31,500
Expected price/
Gross Revenue, $
A uplands 50,500
B ridge 63,000
Soybeans
Gross Revenue, $**
A uplands
B ridge
Wheat
Gross Revenue, $**
A uplands
B ridge
TOTAL GROSS
REVENUE, $
A uplands 50,500
B ridge 63,000
Notes: C - corn,- CB -
105 99.75
130 130
130 104
1,000 1,000
1,000 1,000
1,000 1,000
25,250 23,937.50
31,500 31,500
31,500 25,000
50,500 47,875
63,000 63,000
50,500 47,875
63,000 63,000
63,000 50,000
110.25 110.25
136.50 136.50
136.50 136.50
5.63 5.63
5.63 5.63
28.13 28.13
13,775.62 13,775.62
17,056.87 17,056.87
17,034.37 17,034.37
27,551.24 27,551.24
34,113.74 34,113.74
34,068.74 34,068.74
18,750 18,750
25,000 25,000
25,000 22,500
46,301.24 46,301.24
59,113.74 59,113.74
59,068.74 59,068.74
110.25
136.50
122.85
125
5.63
5.63
28.13
13,775.62
17,056.87
15,328.12
2
27,551.24
34,113.74
30,656.24
16,875
23,750
20,000
44,426.24
57,863.74
50,656.24
115.50
143
143
62 50
2.81
2.81
21.09
7,215.94
8,934.69
8,916.41
2
14,431.88
17,869.38
17,832.82
8,437.50
11,875
10 , OOO
7,031.25
13,215
15,000
15,000
43,025.63
51,775.63
49,864.07
115.50
143
143
62.50
2.81
2.81
21.09
7,215.94
8,934.69
8,469.54
2
14,431.88
17,869.38
16,939.08
3,437.50
11,875
10,000
7,031.25
13,215
15,000
15,000
43,025.63
51,775.63
48,970.32
112
137
137
250
1,000
1,000
1,000
27,000
33,250
33,250
2
54,000
66,500
66,500
54 , 000
66,500
66,500
112
137
137
250
1,000
1,000
1,000
27,000
33,250
33,250
2
54,000
66,500
66,500
54,000
66,500
66,500
117.25
143.50
129.85
125
5.63
5.63
28.13
14,650.62
17,931.87
16,203.12
2
14,644.99
17,926.24
32,406.24
18,125
25,000
21,250
47,426.24
60,863.74
53,656.24
corn-bean; CBWM - corn-bean-wheat-meadow.
, e to lacK o* ^«o«^»-i^i^» t-^-*»»*-nM>nt- Assume 10 bu/acre loss on 40% of
on 5% and 7.5%, respect]
ively, of corn acreage
^..Sn^H» Insurance, Potential Management Tool for Corn Insects,"
acreage for continuous corn.
for the lowlands. Assume '
in Bulletin
For CB
1.5 bu/acre loss on 1%
and CBWM,
of acreage f
T*,CB
of the Entomological Society of America, Vol. 23, No. 3,
pp. 181-184, September 1977.
• • For derivation see Table A-ll; see footnotes, Table A-ll.
-------
Table A-12P. Suamary — No Insecticide Treatment Alternative
Item
Tillage Practices
C Conv. C Chisel
c ::=-tiii
. Rotations
C3 Conv. CB Chisel CB No-till Pa
CBWM
rt. No-till
CBWM
No-till, Herb
i : :: — _
A uplands
B ridge
C uplands
Costs
Tractor (excl.
fuel)
(excl. fuel)
Fuel
Seed
A uplands
B ridge
C lowlands
A uplands
B ridge
C lowlands
A uplands
B ridge
C lowlands
Labor
Other***
A uplands
B ridge
C lowlands
Total Cost (Net
Land Cost)
A uplands
B ridge
C lowlands
Land Costs)
A uplands
a ridge
C lowlands
Notes: C = corn
50,500
63,000
63,OOO
4,604.91
10,643.65
1,426.99
2,380
2,620
2,860
7,540
8,487.50
8,487.50
3,377.50
2,897.50
3,860
2,149.42
0
4,956.42
6,022.10
6,077.61
of
37,078.89
38,852.07
40,110.08
13,421.11
24,147.93
22,889.92
; CB - corn-*
50,500
63,000
63,000
4,537.42
10,365.66
1,336.54
2,380
2,620
2,860
7,540
8,487.50
8,487.50
3,377.50
2,897.50
3,860
2,019.30
0
i —
4,951.40
6,017.08
6,072.59
36,507.82
38,281
39,539.01
13,992.18
24,719
23,460.99
bean ; CBWM
47,875
63,000
50.0OO
4,281.19
8,913.07
1,120.36
2,500
2,740
2,980
7,947.50
9,007.50
9,007.50
6,695
6,047.50
7,340
1,708.98
0
4,908.38
6,185.55
5,214.08
38,074.98
40,004.15
40,565.69
9,800. O2
22,995.85
9,434.31
46,301.24
59,113.74
59,068.74
4,272.26
11,134.31
1,073 97
2,540
2,660
2,780
4,658.75
5,106.25
5,106.25
3,408.75
2,756.25
4,042.50
1,691.76
0
2,893.92
3,432.23
3,490.10
31,673.72
32,126.78
33,591.15
14,627.52
26,986.96
25,477.59
46,301.24
59,113.74
59,068.74
4,301.95
10,828.87
1 113 78
2,540
2,660
2,780
4,658.75
5,106.25
5,106.25
3,408.75
2,756.25
4,042.50
1,671.68
0
2,896.13
3,434.44
3,492.31
31,419.91
31.873.22
33,337.34
14,881.33
27,240.52
25,731.40
44,426.24
57,863.74
50,656.24
4,056.64
9,376.28
2,667.50
2,787.50
2 , 907 . 50
4,843.75
5,351.25
5,351.25
4,931.25
4,218.75
5,627.50
1,361.36
0
2,970.24
3,510.58
3,300.65
31,105.12
31,560.46
32,906.28
13,321.12
26,303.28
17,749.96
43,025.63
51,775.63
49,864.07
4,734.71
14,493.38
1.508.40
2,882.50
2,942.50
4,000.63
4,180.63
4,180.63
1,778.76
1,442.51
2,105
2,215.42
0
1,748.71
2,026.59
2,055.22
33, 362. SI
33,544.14
34,295.28
9,663.12
18,231.49
15.568.79
= corn-bean-wheat-meadow.
43,025.63
51,775.63
48,970.32
4,672 41
13,728.36
1.429.40
2,912.50
2,972.50
4,060.63
4,268.13
4,268.13
2,419.33
2,083.13
2,745.63
2,095.30
0
1,772.76
2,052.74
2 , 009 . 88
33,019.25
33,302.48
33.932.12
9,934.38
18,473.15
14,988.20
54,000
66,500
66 500
4 604 91
10,643.65
1,426.99
2,380
2,620
7,540
8,487.50
8 487 50
3,377.50
2,897.50
3,860
6,460
5,236.42
6,302.10
6,357 61
43,818.89
45,592.07
46,850.08
10,181.11
20,907.93
19,649.92
54 , 000
66 , 500
10,365.66
1,336.54
2,380
2,620
7,540
8,187.50
3,377.50
2,897.50
3,860
6,460
5,231.40
6,297.08
43,247.82
15,021
46,279.01
10,752.18
21,479
20 020 99
47,426.24
60,853.74
9,376.2$
893. 1C
2,667.50
2,787.50
4,843.75
5,351.25
4,931.25
4,218.75
5,627.50
6,460
3,110.24
3,650.50
3 440 65
37,705.12
3B,160.4G
9,721.12
22,693.28
*From Table A-11F, **From Table A-8F, ***From Table A-10F.
-------
Table A-13F
Net Revenue Ranking — No Insecticide Treatment Alternative
Uplands
Lowlands
All Soils
high CB Chisel CB Chisel CH Chisel r
CB Conv. CB Conv. CB Conv. r
C Chisel CB No-till C Chisel r
C Conv. C Chisel C Conv. 1
CB No-till C Conv. C Chisel-Ter. 1
C Chisel-Ter. C No-till C Conv.-Ter. r
C Conv.-Ter. CB No-t.-Ter. CB No-till r
CBWM-Herb. C Chisel-Ter. CBWM-Part. 1
C No- till C Conv.-Ter. CBWM-Herb. r
CB No-t.-Ter. CBWM-Herb. CB No-t.-Ter. 1
low CBWM-Part. CBWM-Part. C No-till r
r
r
1
1
r
r
1
1
1
u
u
1
u
u
u
u
u
u
u
CB
u
1
-<-
CB Chisel
CB Conv.
CB No-till
-«-
CB Chisel
CB Conv.
C Chisel
C Conv. ^
C Chisel
C No-till
C Conv.
CB No-till-Ter.
C Chisel-Ter.
-«-
C Conv.-Ter.
C Chisel-Ter.
C Conv . -Ter .
CBWM-Herb.
CBWM-Part.
CB No-till ^
CBWM-Part
CBWM-Herb .
CB Chisel
CB Conv.
CB No-till-Ter.^
C Chisel
C Conv.
CB No-till ^_
C Chisel-Ter.
C Conv.-Ter. ^
CBWM-Herb .
C No-till
No-till-Ter.
CBWM-Part.
C No-till ,
•<-
28,000
26 , 000
24,000
21,000
19,000
17,000
14,000
13,000
10,000
9,000
Notes: C = corn; CB = corn-bean; CBWM = corn-bean-wheat-meadow.
r = ridge; 1 = lowlands; u = uplands.
163
-------
few shifts in financial return position as a result of eliminating insec-
ticide treatment altogether. The continuous corn no-tillage options on
the ridge and uplands moved up in the ranking because they bear the heavi-
est pesticide costs in the base case and this alternative relieved this
burden somewhat. Net revenue improves for all options and particularly
for the continuous corn no-tillage options, one of which remains at the
bottom of the ranking, however. Other shifts in position that occur
when all soils are ranked together are a result of slight differences
in gain or loss from the decreased revenue and decreased pesticide costs
and are not especially significant.
The same conclusions can be drawn from this alternative as from
Alternative E, namely, that insecticide costs are not significant relative
to other productions costs and therefore even total elimination of these
costs (which account for at most 10 percent of total costs) will not
affect the farmer's choice of farming practice. This is true even though
there is a reduction in yield caused by the lack of pesticide use; the
decreased pesticide costs more than make up for the lost revenue. As
with Alternative E, it might be worthwhile to combine the no insecticide
treatment alternative with other alternatives that have been considered
such as the increased energy scenario.
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Allen County Soil and Water Conservation District. "Cost Sharing Formulae
For Land Use Practices in Black Creek," effective January 1976, mimeo.*
Allen County Soil and Water Conservation District. "Land Use Practices in
Black Creek," mimeo.*
Beuerlein, J.E. and Bone, S.W. "Selecting a Tillage System." Ohio State
University, Cooperative Extension Service. (Undated).
164
-------
Bone, S.W. et al. Reduced Tillage Systems for Conservation and Profitability.
Ohio State Department of Agricultural Economics, April 1976.
Brink, L.; McCarl, B.A. and Doster, D.H. Methods and Procedures in the
Purdue Crop Budget (Model B-9); An Administrator's Guide. Station Bulletin
No. 121. Purdue University, Department of Agricultural Economics, March 1976.
Carlisle, G.W. and Griffith, D.R. "Conservation Tillage Trials in Progress in
the Black Creek Watershed." Proceedings, Best Management Practices for
NPS Pollution Control Seminar, Chicago, Nov. 16-17 1976.
Data Resources Inc. "Data Resources Outlook for the United States Energy
Sector: Control Case." Energy Review. Lexington, Mass. Summer 1977.
Doster, D.H. Indiana Custom Rates for Power-Operated Farm Machines—1976.
EC-130 (Rev). Purdue University, Cooperative Extension Service, West
Lafayette, Ind.
Doster, D.H. "Purdue B-94 Linear Program Crop Budget—Explanation of Base
Case Farm." Summary. Purdue University, Department of Agricultural
Economics (Undated).
Doster, D.H. and Macy, T. "The Time Resource in the Cornbelt Farm Equipment
Selection; Twenty-Four Years of Weather Data." Purdue University,
Department of Agricultural Economics, July 1977.
Edwards, C.R. and Matthew, D.L. "Soil Insects Affecting Corn." Pub E-49.
Purdue University, Cooperative Extension Service, West Lafayette, Ind.,
January 1977.
Goettl, D.L. "Total and Unit Costs for Tile Outlet Terraces in Black Creek."
Memo to Dan McCain, USDA. February 8, 1977.
Gordon, J.R. and Quinn, T.R. Profit of Employment and Income in Indiana. EC-421,
Purdue University, Cooperative Extension Service; West Lafayette, Ind., 1973.
Griffith, D.R.; Mannering, J.V. and Moldenhauer, w.c. Conservation Tillage
in the Eastern Corn Belt. Journal of Soil and Water Conservation,
Vol. 32, No. 1, January-February 1977.
Griffith, D.R. and Manning, J.V. "Where is No Plow Tillage Adopted in
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Guide. AY-18. Purdue University, Cooperative Extension Service, West
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165
-------
Iowa State University, Cooperative Extension Service. Background Information
for use with Corp-Opt System. FM 1628, 8th revision, Ames, Iowa,
December 1976.
Iowa State University, Cooperative Extension Service. Crop-Opt System Input
Farms. FM 1627, 8th revision, Ames, Iowa, December 1976.
Iowa State University, Cooperative Extension Service. Estimated 1977 Iowa
Farm Custom Rates. FM 1698, Ames, Iowa, Revised January 1977.
Iowa State University, Cooperative Extension Service. Estimating Farm
Machinery Costs. PM-710, Ames, Iowa, November 1976.
Iowa State University. "Worksheet for Estimating Farm Machinery Costs."
PM-710a, Ames, Iowa, November 1976.
James, Sydney C., ed. Midwest Farm Planning Manual. Ames, Iowa: Iowa
State University Press, revised 1975.
Johnson, K.T. and White, W.C. Energy Requirements for the Production of
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Transport of Various Pesticides. Draft Paper. New York State College
of Agriculture and Life Sciences; Ithaca, N.y. November 21, 1977.
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Office of Research and Development. February 1977.
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EC-310, West Lafayette, Ind., September 1975.
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Management. ID-68, Revised, West Lafayette, Ind., 1975.
Purdue University, Cooperative Extension Service. Hired Farm Labor. EC-459
April 1977.
Purdue University, Cooperative Extension Service. Indiana Custom
Rates for Power Operated Farm Machines 1976. EC-130, Revised.
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Field Machines." Purdue University, Cooperative Extension Service, AE-81.
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166
-------
Triplett, G.B. Jr. and Van Doren, D.M. Jr. "Agriculture Without Tillage."
Scientific American, 1977.
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Bulletin of Entomological Society of America, Vol. 23, No. 3, September 1977,
Turpin, F.T.; Dumenil, L.C. and Peters, D.C. "Edaphic and Agronomic
Characters that Affect Potential for Rootworm Damage to Corn in Iowa."
Journal of Economic Entomology, Vol. 65, No. 6, December 1972.
Turpin, F.T. and Maxwell, J.D. "Decision-Making Related to use of Soil
Insecticides by Indiana Corn Farmers." Journal of Economic Entomology,
Vol. 69, No. 3, June 1976.
Turpin, F.T. and Thieme, J.M. "Impact of Soil Insecticide Usage on Corn
Production in Indiana: 1972-1974". Journal of Economic Entomology, 1977.
U.S. Department of Agriculture. Crop Reporting Service.
Agricultural Prices, Annual Summary 1976. Pr 1-3(77).
Agricultural Prices Released February 28, 1977. Pr 1(2-77).
Agricultural Prices Released April 29, 1977. Pr 1(7-77).
Agricultural Prices Released July 29, 1977. Pr 1(7-77).
Agricultural Prices Released October 31, 1977. Pr 1(10-77).
U.S. Department of Agriculture. Economic Research Service, Farm Costs and
Returns, Agriculture Information Bulletin, No. 230, Washington, D.C.,
September 1968.
U.S. Department of Agriculture. Environmental Protection Agency. Office of
Research and Development, Control of Water Pollution from Cropland,
Vol. 1: A Manual for Guideline Development, EPA 600/2-75-026a, November
1975. Vol. II: An Overview, EPA 600/2-75-0266, June 1976.
U.S. Department of Agriculture. Indiana Soil Conservation Service. Technical
Guide, Section V, Installation and Amortized Costs for Major Conservation
Practices. Technical Guide, Section V, Indiana, April 1975.
U.S. Department of Agriculture. Soil Conservation Service, installation and
Amortized Costs for Major Conservation Practices. Technical Guide,
Section V, Indiana, April 1975.
U.S. Department of Agriculture. Soil Conservation Service. Total and
Unit Costs for Tile Outlet Terraces in Black Creek. Memo from Dan McCain
to Deone L. Goettl, February 8, 1977.
U.S. Department of Agriculture. Soil Conservation Service. "No Flow
Tillage—A Conservation Tool for Indiana Farmers." AY 192. Purdue
University, Cooperative Extension Service, West Lafayette, Ind.
167
-------
U.S. Department of Agriculture. Soil Conservation Service. Allen County
Soil Conservation and Water District. Soil Conserving Tillage Systems.
(Undated).
U.S. Department of Agriculture. Statistical Reporting Service. Annual Crop
and Livestock Summary, 1976; Indiana Crop and Livestock Statistics.
A-77-1, Purdue University, Agricultural Experiment Station, West
Lafayette, Ind., August 1977.
White, W.C. Energy Problems and Challenges in Fertilizer Production. Paper,
The Fertilizer Institute, Washington, D.C., December 4, 1974.
White, W.C. Fertilizer—Food-Energy Relationships. Paper, The Fertilizer
Institute, Washington, D.C., August 28, 1974.
Wilson, C.D. Environmental Impact of Land Use on Water Quality. Operations
Manual. Black Creek Study, Allen County Indiana. EPA-905-74-002. U.S.
Environmental Protection Agency, Region V, Allen County Soil and Water
Conservation District, March 1974.
* Informal mimeos entitled by Meta Systems, Inc.
168
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Appendix B
Methods for Predicting Watershed Loadings
Introduction
The methods described below have been developed to assess the
impacts of agricultural practices on nonpoint pollutant loadings. The
models are of an empirical nature and are concerned with long-term aver-
age emissions, in the spirit of the Universal Soil Loss Equation (Wisch-
meier and Smith, 1972). Average export rates of the following substances
are evaluated in surface runoff and in subsurface drainage:
1) Sediment (sand, silt, and clay fractions);
2) Phosphorus (extractable particulate and soluble);
3) Soluble nitrogen; and
4) Dissolved color.
The computed concentrations of these components are assumed to be repre-
sentative of average water quality conditions in rivers draining the
agricultural watersheds. The methodology is appropriate for linking
with downstream models for the purpose of evaluating quality impacts in
impounded waters, as discussed in a subsequent section (see Methods for
Predicting Impoundment Water Quality).
Using the generalized pathways depicted in Figure B-l, emissions are
computed as functions of the following characteristics:
169
-------
WATERSHED
CHARACTERISTICS
Field Characteristics
Soil Characteristics
Climate
Morphorr.'jtry
AQf'iculturai
Practices
Crop Yields
TRANSPORT RATES
Sediment
Runoff
Percolation
TRANSPORT MEDIA
COMPOSITION
Sediment
Runoff
Percolation
Nitrogen Budget
AVERAGE RIVER
WATER QUALITY
AND COMPONENT
LOADINGS
Sediment
Phosphorus
Nitrogen
Color
Figure B-l. Pathways in Predicting Watershed Loadings
-------
1) Surface Soil Properties
a) Erodibility (K factor in USLE, Wischmeier and Smith, 1972);
b) Texture (sand, silt, and clay content);
c) Hydrologic Soil Group (SCS/USDA, 1971);
d) Extractable phosphorus content (in each texture class);
e) Phosphorus distribution coefficient (g extractable P/kg
Soil)/(g dissolved P/m3 soil solution); and
f) Organic matter content (in each texture class).
2) Watershed/Field Properties:
a) Slope;
b) Slope length;
c) Surface area;
d) Total flow (runoff and drainage); and
e) Rainfall erosivity (R factor in USLE);
3) Agricultural Practices:
a) Cropping factor (C in USLE)
b) Practice factor (P in USLE)
c) Nitorgen and Phosphorus fertilization rates;
d) Tillage depth; and
e) Crop residue management.
The methodology is based upon the Universal Soil Loss Equation (USLE),
which has been developed by the USDA for use in the soil conservation
area. This equation and its tabulated parameter estimates are based
upon a large data-collection and analytical effort. A number of addi-
tions have been made in this study in order to make the USLE a more use-
ful tool for evaluating water quality impacts. The formulations and
171
-------
calibrations of the additional functions are based upon substantially
less data and analysis than the USLE and could therefore be described
as less objective. Analysis of further experimental and monitoring data
could lead to a more objective basis for some of the assumed functional
forms and parameter estimates. However, for this study relatively sub-
jective assessments are relied upon,— substantiated when possible with
data and the opinions of experts. A sensitivity analysis will help to
determine which assumptions are most important in evaluating both the
absolute and the relative impacts of agricultural practices on watershed
emissions and on downstream water quality.
The methodology is applicable to a single field or plot of uniform
characteristics. In this preliminary assessment of agricultural prac-
tices, a hypothetical watershed is assumed to be comprised of a number
of fields of equal characteristics. This provides a rough measure of
the unit emissions and water quality impacts of a given field/soil type/
agricultural practice combination. The methodology could be applied to
a heterogeneous watershed consisting of a number of areas, each with its
own set of field/soil type/practice specifications. The effects of
heterogenous watershed characteristics on practice evaluations and con-
clusions are considered higher level questions, which are best addressed
subsequent to an analysis of homogenous watersheds.
In order to be compatible with the economic analysis carried out in
this study the models are calibrated to three different field/soil types
which are characteristic of the Black Creek Watershed, Indiana. A
research and demonstration program sponsored by the EPA (Christenson and
172
-------
Wilson, 1976, Lake and Morrison, 1975, has provided some data for cali-
brating the models to these three field and soil types. In the discus-
sion below, general (i.e., process-related) parameter estimates are pre-
sented immediately after the corresponding functions. Soil- and practice-
specific parameters are presented and discussed in a separate section. In
view of the preliminary nature of many of the functional forms and parame-
ter estimates, a final sensitivity analysis is essential to understanding
and assessing the feasibility of applying the methodology in a planning
context.
Sediment
Estimation of gross sheet and rill erosion rates are obtained
through use of the Universal Soil Loss Equation (Wischmeier and Smith, 1972)
S = .224 RKL PC (1)
where,
S = gross erosion rate (kg/m -yr)
R = rainfall erosivity factor
K = soil erodibility factor (tons/acre-year)
L = length/slope factor
S
P = practice factor
.224 = dimensional factor ((kg/m2)/(tons/acre))
The C factor is computed considering the seasonal variations in soil
cover and rainfall erosivity, as prescribed by Wischmeier and Smith.
Detailed discussions of the bases, assumptions and parameter estimates
173
-------
of this model are available elsewhere (Wischmeier and Smith, 1972,
Wischmeier, 1976, EPA and USDA, 1975, and MRI, 1976).
The length/slope factor is computed using the following function
(Wischmeier and Smith, 1972):
L = /T (.0076 + .0053g + .0076g2) (2)
s
where,
L = length of slope (feet)
g = slope gradient (percent)
Eroded sediment is usually enriched in fine particles, relative to
the surface soil of its origin. This enrichment is apparent in edge-of-
field sediment measurements (Soltenberg and White, 1953; Kilner, 1960),
in river sediments measurements (Rausch and Heinemann, 1975; Jones et al,
1977) and in lake bottom sediment measurements (Stall, 1972). Since
finer fractions of soil have higher surface areas per unit mass, they
generally have higher adsorption capacities and higher nutrient and
organic matter contents, expressed as grams per gram of solid (Buckman
and Brady, 1960).
Enrichment of fine particles in sediment is considered here in
order to permit explicit calculation of the nutrient and organic matter
contents of eroded sediment based upon the measured nutrient and
organic matter contents of various soil size fractions. This is an
alternative to the use of gross "enrichment ratios" (MRI, 1976).
By explicitly considering the clay, silt, and sand fractions in soil
174
-------
and eroded sediment, differences in the behavior of these fractions in
rivers and in impoundments can be modeled. This also forms a basis for
future development of models for other constitutents, such as biocides
or biocide residues, which may also show preferential adsorption to
fine particles.
The enrichment phenomenon has been shown to increase with decreas-
ing gross erosion and runoff rates. For instance, Stoltenberg and White
(1953) found that the clay content of eroded material from a soil con-
taining 16 percent clay increased from 25 percent to 60 percent as run-
off rates decreased from 2.84 to .01 inches/hour. Raush and Heinemann
(1976) found that the clay fraction in river sediment from a watershed
in Missouri increased from 30 percent to 80 percent as peak storm flows
decreased from 10 to .3m3/sec. An empirical function for computing
phosphorus enrichment ratios developed by Massey et al (1953) and pre-
sented by MRI (1976) is qualitatively consistent with this behavior, in
that it predicts an increase in the phosphorus enrichment ratio, given
a decrease in either the total sediment concentration or the total
erosion rate.
In order to account for enrichment, the texture of eroded sediment
is computed as a function of soil texture and S, the gross erosion rate,
using the following assumed relationships:
XCL = XCL + (XSL - XCL} < Kl ) <3>
CL CL CL CL
175
-------
= X (TT--JT) (4)
SA SA S + K2
CL - XSA
xs
YM _ n _§
XCL - I - 1E
where,
X , X , X = clay, silt, and sand fractions of eroded sediment
CL SX SA
S S S
X , X , X = clay, silt and sand fractions of surface soil
wJ-i S X S A
Ki, K2, KB = empirical parameters
X = maximum clay content of eroded sediment
According to these equations, sediment texture approaches that of surface
soil as S approaches infinity, while the clay, silt, and sand fractions
M M
approach X , 1 - X , and 0 as S approaches zero. The following tentative
CL CL
parameter values are assumed:
Kj = .50 kg/m2 - year
K2 = 20.0 kg/m2 - year
K3 = 2.0
176
-------
The behavior of sediment texture as a function of S for these parameter
values and for a typical soil texture is depicted in Figure B-2. While
explicit, quantitative justification for the assumed parameter values
cannot be given, sediment texture computed according to this scheme
agrees qualitatively with the data discussed above. Direct calibration
and testing should be done, when the appropriate data are available.
Estimates of gross erosion for each texture class are converted to
watershed emission rates by application of a sediment delivery ratio,
which is computed as a function of downstream watershed area and texture
class:
SCL - SXCL DdCL (7)
Ssi - sxsi Ddsi
SSA
where,
°, S° , S^_ = delivered clay, silt and sand (kg/m2 - year)
CL SX SA
D = reference delivery ratio
d , d , d = delivery ratio multiplier for clay, silt and
C»L S X S A
sand fractions.
Total watershed area has been often used as an independent variable for
predicting mean sediment delivery ratios (EPA/USDA, 1976; Vanoni, 1975)
177
-------
H1
-j
GO
1.0
.8
o>
2
X
c .4
0)
£
T3
-------
Data from a table in EPA/USDA (1976), have been fit to the following empiri-
cal function:
5 = K4 A;KS do
where
Kt» = .34
K5 = .20
i)
A = total watershed area (km )
5 = mean delivery ratio
While other factors have been employed as delivery ratio predictors, the
above functional form has been most widely used (Vanoni, 1975). In a
heterogeneous watershed, however, direct application of equation (10)
to the area mean gross erosion rate could lead to errors, because it
does not take into account the fact that delivery ratios are likely to
be higher in the lower contours of a watershed than in the upper contours,
due to shorter transport distances. This can be demonstrated quantita-
tively. By differentiating the product of the total watershed area and
the average delivery ratio (computed according to equation (10)), it can
be shown that equation (10) implies the following:
179
-------
where,
D = localized delivery ratio for a region at the uppermost con-
AD
tour of a watershed
A
A = watershed area downstream (km )
D is a localized delivery ratio, whereas D, in equation (10), repre-
D
sents the average value over an entire watershed. Equation (11) pre-
dicts lower effective delivery ratios in higher areas within a given
watershed. For application in heterogeneous watersheds, the D value in
equations (7) to (9) should be computed for each sub-area using equation
(11) and the downstream watershed area, as opposed to the total water-
shed area. In homogeneous watersheds, results are independent of
whether equation (10) or equation (11) is used.
A graph in MRI (1976) indicates that delivery ratios for clay, silt,
and sand are approximately in the ratios 5:3:1. If these ratios are
normalized to a d . value of 1, the following delivery ratio multipliers
S J.
are calculated:
= 1.67
dsi = i.oo
dSA ' °'
These multipliers are assumed to be independent of location in a given
watershed.
180
-------
The total sediment load transported to a downstream impoundment is
computed as the sum over the texture classes multiplied by the ratio of
watershed area to impoundment surface area:
where
D 2
2
S = impoundment sediment load (kg/m surface area-year)
A = impoundment surface area (km2)
The computed sediment delivery of each texture class is used to estimate
sedimentation rate, phosphorus trapping rate, and suspended solids con-
centration in the impoundment, according to the methodology discussed
separately (see Appendix C).
Runoff and Percolation
Predictions of the emissions of soluble phosphorus and color are
dependent upon estimates of average surface runoff rates. The total flow
rate from a watershed or field is assumed to consist of two components,
the sum of which is independent of the agricultural practice:
q - q + q (13)
where
q = total flow rate (m/year)
qR = surface runoff rate (m/year)
qD = subsurface drainage (m/year)
181
-------
This essentially assumes that average evapotransporation rates are not
significantly influenced by the mode of farm operation. The runoff
component, q , is evaluated as:
K
qR - qd-fR) (14)
where
q = baseline runoff rate for straight-row, continuous corn on soil
R,
of the appropriate hydrologic group (m/year)
f = runoff reduction factor appropriate for agricultural practice
K
and soil type.
This method is based upon the results of simulations performed by Wool-
hiser (1975, 1977), using a modification of the SCS Curve Number runoff
model (SCS, 1971). These simulations have provided regional estimates
of average annual runoff rates for soils in various Hydrologic Groups
(SCS, 1971) and for two basic agricultural practices: straight row,
continuous corn and continuous meadow, which represent the approximate
upper and lower limits of q , respectively, as influenced by agricultural
K
practice. The former are used here as reference values and equated to
q for the appropriate soil group and region. Some of Woolhiser's simu-
lation results are summarized in Table B-l. Regional variations in q are
shown in FiguresB-3 through B-6 for soils in various Hydrologic Groups.
Values of f are sensitive both to soil type and to agricultural
I\
practice, since some practices are only effective on certain soil types.
Estimation of f values is based upon Woolhiser's Table 14 and Figure 32
I\
182
-------
Table B-l. Results of Direct Runoff Simulations (EPA/USDA, 1975)
Location
Wichita, KS
Columbia, MO
Columbus, OH
Des Moines, IA
Grand Isl., NB
Sioux Fall, SD
Cairo, 1L
Indianapolis, IN
Springfield, IL
Houston, TX
Raleigh, NC
Charleston, WV
Birmingham, AL
Columbia, SC
Dallas, TX
Little Rock, AR
Buffalo, NY
Boston, MA
Scranton, PA
Pittsburgh, PA
Seattle, WA
Hydro logic
soil group
B
D
C
B
B
B
B
C
B
D
B
C
B
B
D
D
B
A
C
C
B
Estimated
1
direct runoff
(inches)
2.2
5.3
3.6
1.6
1.5
1.2
4.7
5.2
2.6
11.3
2.4
4.0
7.2
4.4
8.3
13.4
1.5
2.2
2.6
3.2
2.9
% reduction in annual runoff
Contouring,
R9
11
20
12
18
16
8
1
11
12
17
16
14
11
17
15
12
13
6
16
10
20
Contoured and
terraced, R 9, R 1 2
22
37
21
27
23
16
9
21
22
36
32
25
21
31
32
24
23
15
30
19
35
Meadow
R16
81
75
75
89
88
94
78
75
89
52
88
75
72
83
55
58
89
94
82
83
85
Estimated
mean growing
season direct
runoff (inches)
1.7
2.9
1.0
0.9
0.9
0.7
1.3
1.7
1.4
5.9
1.1
1.2
1.8
2.3
5.1
5.5
0.7
0.6
0.8
0.9
0.1
% reduction in growing season runoff
Contouring,
R9
15
31
10
24
12
13
11
23
12
17
19
25
14
21
14
11
33
11
21
22
33
Contoured and
terraced, R 9, R 1 2
29
53
24
38
26
28
24
42
24
36
39
36
29
39
29
24
54
26
32
41
55
Meadow
R16
80
68
73
85
90
95
80
74
83
49
88
62
74
82
53
57
100
85
78
85
89
00
OJ
1 More than 4,000 soils in the United States and Puerto Rico have been assigned by the Soil Conservation Service to Hydrologic soil groups A through D on the basis of their
runoff potential. Hydrologic group A has low runoff potential; group D has a high runoff potential; and B and C are intermediate. For a more detailed discussion, see Volume II,
Appendix A.
-------
00
Figure B-3.
Mean Annual Potential Direct Runoff in Inches. Straigt-row Corn in Good
Hydrologic Condition — Hydrologic Soil Group A. (woolhiser 1976)
-------
00
'/.o
Figure B-4.
Mean Annual Potential Direct Runoff in Inches. Straight-row Corn
in Good Hydrologic Condition — Hydrologic Soil Group B.
(Woolhiser, 1976)
-------
00
0
Figure B-5.
Mean Annual Potential Direct Runoff in Inches. Straight-row Corn
in Good Hydrologic Condition — Hydrologic Soil Group C
(Woolhiser (1976)
-------
00
Figure B-6.
Mean Annual Potential Direct Runoff in Inches. Straight-row Corn
in Good Hydrologic Condition — Hydrologic Soil Group D.
Woolhiser (1976)
-------
(EPA/USDA, 1975) , which are reproduced here as Table B-2 and Figure B-7,
respectively. In the former, the effectiveness of various practices in
reducing runoff are qualitatively evaluated as "slight," "moderate,"
and/or "substantial." Figure B-7 provides a basis for obtaining semi-
quantitative estimates of f values from the indications provided by
R
Table B-2.* The latter are interpreted considering the characteristics
of the soil and any local experimental or monitoring data. Woolhiser's
simulations and hence this procedure are less reliable in areas in which
snowmelt is a dominant hydrologic factor (Woolhiser, 1975) .
The subsurface drainage, or percolation rate is estimated by
difference:
Estimates of q values are obtained from regional streamflow records. A
typical value for the Cornbelt is .25 m/year.
Phosphorus
Phosphorus emissions are estimated as the sums of three separate
components: extractable particulate, soluble, and soluble phosphorus
leached from surface crop residues during snowmelt. Only the NH^F/HCl
extractable portion of the particulate phosphorus (Bray P) is included.
* The reduction factors in Figure B-7 are related to mean growing season
potential direct runoff, which can be estimated from mean annual potential
direct runoff by comparing the appropriate columns in Table B-l. The per-
centage reductions are assumed to be appropriate for both time scales.
(See Table B-l.)
188
-------
Table B-2. EFFECTS OF VARIOUS PRACTICES ON DIRECT RUNOFF (EPA/USDA, 1975)
No.
R 1
R2
R3
R4
R5
R6
R7
R8
R9
R 10
R 11
R 12
R 13
R 14
R 15
R 16
R 17
R 18
Runoff Control Practice
No-till plant in prior crop residues
Conservation tillage
Sod-based rotations
Meadowless rotations
Winter cover crop
Improved soil fertility
Timing of field operations
Plow plant systems
Contouring
Graded rows
Contour strip cropping
Terraces
Grassed outlets
Ridge planting
Contour listing
Change in land use
Other practices
Contour furrows
Diversions
Drainage
Liindforininp
Construction of ponds
Practice Highlights
Variable effect on direct runoff from substantial reductions to
increases on soils subject to compaction.
Slight to substantial runoff reduction.
Substantial runoff reduction in sod year; slight to moderate
reduction in rowcrop year.
None to slight runoff reduction.
Slight runoff increase to moderate reduction.
Slight to substantial runoff reduction depending on existing
fertility level.
Slight runoff reduction.
Moderate runoff reduction.
Slight to moderate runoff reduction.
Slight to moderate runoff reduction.
Moderate to substantial runoff reduction.
Slight increase to substantial runoff reduction.
Slight runoff reduction.
Slight to substantial runoff reduction.
Moderate to substantial runoff reduction.
Moderate to substantial runoff reduction.
Moderate to substantial reduction.
No runoff reduction.
Increase to substantial decrease in surfiicc nmoff.
hiaiMxe to slij'.lit runoff reduction.
None to substantial runoff reduction. Relatively expensive.
Good pond sites must be available. May be considered as a
treatment device.
This is considered by some to be a measure of the "available" particulate
phosphorus (Romkens and Nelson, 1974). The remaining inorganic and organic
particulate forms are assumed to be unavailable to support algal growth in
downstream impoundments. Extractable and total particulate phosphorus data
from soils in the Black Creek area (Sommers et al, 1975) generally support
Taylor's (1967) suggestion that about ten percent of the phosphorus in soils
189
-------
is available for aquatic plant growth. Other investigators have used other
definitions of "available P" which would correspond to lower percentages of
total P (Porter, 1975). This is an important assumption which is critical
to evaluating the effects of erosion controls on eutrophication and requires
additional study.
The first step in estimating phoshorus emissions is to evaluate
the extractable phosphorus content of the surface soil as a function of
100 r
90
80
70
I 60
o
•o
o>
c
OJ
o>
a.
50
40
30
20
10
Reduction Achieved by Changing from Row
Crop to Continuous Meadow
(SCS Method)
Substantial Reduction Zone
Moderate Reduction Zone
Slight Reduction Zone
01 23456789 10
Mean Growing Season1 Potential Direct Runoff (inches)
Figure B-7. Definition of Ranges of Reduction in Mean
Growing Season Direct Runoff (EPA/USDA, 1975)
190
-------
fertilization rate, tillage depth, and baseline soil phosphorus levels.
Direct measurements of the extractable phosphorus contents of the vari-
ous soil size fractions are relied upon for model calibration. The base-
line, average soil phosphorus level is computed from:
where
P° = baseline, average phosphorus content of surface soil (gP/kg soil),
P , PCT, PC = baseline extractable phosphorus content of clay,
\*Li o X oA
silt, and sand fractions in surface soil (gP/kg
solid).
The rates and depths of phosphorus addition to surface soils have
been observed to influence the surface soil phosphorus content (Timmons,
et al, 1973; Brigham, 1977; Rfimkens and Nelson, 1974; Romkens, et al,
1973). A nearly linear relationship between the rate of fertilizer
addition and the concentration of available phosphorus in surface soil
has been reported by Romkens and Nelson (1974). Timmons, et al. (1973)
detected increases of about .005 and .035 g available P/kg in eroded
sediment from plots receiving equal fertilizer doses which were plowed
under and surface broadcast, respectively. These increases are relative
to unfertilized plots, the sediment from which averaged about .010 gP/kg.
By decreasing the depths of fertilizer incorporation, use of minimum
tillage methods causes an increase in the surface soil phosphorus level,
which tends to offset the benefits of such practices as means of con-
trolling phosphorus losses through erosion (Brigham, 1977).
191
-------
The increase in surface soil phosphorus over baseline levels due to
fertilization and tillage method is estimated as follows:
where
AP = increase in surface soil phosphorus (gP/kg soil)
F = fertilization rate (gP/m2 - yr)
p = surface soil density (kg/m3)
Z = effective tillage depth (m)
K = empirical parameter (yr)
The empirical parameter K, accounts for removal and conversion of fertilizer
phosphorus into unavailable forms . The inverse of K, is a measure of the
o
fraction of the added fertilizer phosphorus which is recoverable as avail-
able soil phosphorus. Laboratory studies by Romkens and Nelson (1974) have
given fractions ranging from .25 to .76 for various soil types. A value
of .50 is assumed here, corresponding to a Kfi value of 2 year . Combined
3
with a p,, estimate of 1300 kg/m (Buckman and Brady, 1960), this gives in-
creases of .037 and .005 gP/kg for minimum tillage (Z = 1 inch = .025m) and
conventional tillage (Z = 7 inches = .18m), respectively, when a typical
2
fertilization rate of 2 gP/m -yr is used. These results are in line with
those of Timmons et al. (1973), as discussed above.
With the increase in surface soil phosphorus level computed according
to the above scheme, corresponding increases in the phosphorus content of
192
-------
each texture class are evaluated as follows:
PS = P° + AP (18)
4
where
g
P = surface soil phosphorus content (gP/kg soil)
S S S
P.,T ,P ,P = phosphorus content of clay, silt, and sand fractions
(gP/kg soil).
The load of sediment phosphorus transported downsteam to the impoundment is
evaluated as the sum over the texture classes:
LP = (SD PS + S° PS + S° PS ) (22)
LPSED (SCL PCL + SSI FSI + bSA PSAJ A ^ '
where,
LP = loading of available phosphorus in sediment
jEjlj
2
(gP/m impoundment surface area-yr)
The second component of phosphorus loading is the soluble fraction, which
is exported from the watershed in surface runoff and subsurface drainage:
LPSOL * («R CR + % V A
193
-------
where
3
C = soluble phosphorus concentration in surface runoff (g/m )
R
3
C = soluble phosphorus concentration in drainage (g/m )
LPom = loading of soluble phosphorus transported to the
oOLi
2
impoundment (g/m -yr) .
The runoff and drainage rates, q and q , respectively, are estimated accord
K D
ing to the methods described previously. Soluble phosphorus concentrations
in surface runoff are computed from the average eroded sediment contents,
assuming a linear adsorption isotherm:
CR= f- (24)
R Yp
E E S E S E S (
P " XCL PCL + XSI PSI + XSA PSA (25)
where
3
= phosphorus distribution coefficient (m /kg)
P = average available phosphorus content of eroded sediment (g/kg) .
Yp is a soil-specific parameter which is evaluated based upon soil available
phosphorus and soluble equilibrium phosphorus concentrations (Taylor and
Kunishi, 1971) . Based upon data from Romkens and Nelson (1974), Yp ranges from
3
.1 to 1m /kg for different soil types. Data from the Black Creek area
3
(Sommers et al, 1975) indicate a range of .5 to 1 m /kg.
Drainage is assumed to be in equilibrium with relatively phosphorus-deficient
3
subsoils. Accordingly, C is set at a relatively low value of .03g/m . This
is typical of soluble phosphorus concentrations in drainage from mostly forested
watersheds in the Cornbelt, from which surface runoff is generally insignifi-
cant (Omernik, 1976).
194
-------
The final phosphorus export component is that which leaches from surface
crop residues during snowmelt periods. This component is soluble and is
considered separately because the phosphorus concentrations in snowmelt runoff
may not equilibrate with frozen surface soils. The freezing, thawing, and
leaching cycle which culminates during initial snowmelt may release substan-
tial quantities of dissolved phosphorus from residues left on the soil surface
after fall harvest. In studies of runoff from natural rainfall erosion plots,
Timmons et al (1968) found that more water-soluble phosphorus was lost in
snowmelt runoff from seedling alfalfa than from other periods or cropping
sequences studied (continuous corn, rotation corn, and rotation oats).
Laboratory studies (Timmons et al, L970) revealed that dne freezing/thawing/
leaching cycle could release 9, 28, 6 and 5% of the total phosphorus in
residues from alfalfa, bluegrass, barley and oats, respectively. Three
consecutive cycles released 36, 64, 13 and 16% of the phosphorus in these
residues, respectively. Timmons, et al (1970) estimated potential emissions
under field conditions based upon the laboratory data obtained for one cycle
and showed that these amounts could be appreciable relative to other soluble
phosphorus losses. A major uncertainly in their estimates is the extent to
which snowmelt phosphorus concentrations may equilibrate with (i.e., be
adsorbed by) partially thawed surface soils or stream bank sediments.*
Despite the relative lack of data in this area, inclusion of this com-
ponent is considered important for evaluating the impacts of tillage methods
on water quality with regard to eutrophication. No-till methods tend to
* Data from the Black Creek Watershed (Nelson, 1977) also indicate high
soluble inorganic phosphorus (SIP) concentrations in snowmelt. At one
sampling station* for instance, the average SIP concentrations in 1976 snow-
melt was .19 g/m , compared with an annual average concentration of .05 g/m .
195
-------
leave crop residues on the surface and thus create a greater potential for
leaching losses in snowmelt than conventional tillage methods, which in-
corporate residues into the soil.
The following function is employed to estimate this component:
LPPES= RESP (1 - FRES> K7 (26>
Where
LP__n = impoundment phosphorus loading attributed to leaching
RES
2
from crop residues during snowmelt (gP/m -yr)
RES = average mass of residue phosphorus on the soil surface
2
after harvest (gP/m )
?_.„„ = fraction of residues plowed under for a given tillage
•RES
method
K_ = fraction of surface residue P leached in snowmelt (year)'1.
A nominal value of 0.01 has been tentatively assumed for K . This value
is low, relative to the range assumed by Timmons, et al (1970), .05 to
.28. A lower value is probably more appropriate, considering the possi-
bility of partial adsorption by surface soils and river bank sediments.
The nominal value has been assumed merely to demonstrate the potential
importance of this component of the available phosphorus losses from
agricultural operations. This, in turn, indicates a need for additional
data in order to permit a more quantitative definition of this component.
The total phosphorus loading is evaluated as the sum of the sediment,
soluble, and snowmelt residue components:
196
-------
LPT - LPSED + ^SOL + LPRES (27)
where
LP = available phosphorus load transported to the downstream
impoundment (g/m2-year).
This value is used to evaluate the water quality response in the impound-
ment with regard to transparency and chlorophyll-a.
Soluble Nitrogen
Because nitrogen is generally more mobile in soil systems than
phosphorus, estimates of average soluble nitrogen export from agricul-
tural areas are based upon mass balances, rather than upon computed soil
erosion rates and adsorption chemistry. Other investigators (Onishi, et al,
1974; Tanji, et al, 1977; Harmeson, et al, 1971) have employed similar
models for the purpose of obtaining rough estimates of potential nitrogen
emissions. A nitrogen mass balance is assumed here to consist of four
input and three output components:
"FX + "FE + *R + *M = *Y + *D + *L (28)
where
N = fixation rate (gN/m2-year)
FX
N = fertilization rate (gN/m2-year)
FE
2
N = rainfall nitrogen input rate (gN/m -year)
197
-------
• 2
N,, = soil mineralization rate (gN/m -year)
• 2
N = crop yield (gN/m -year)
• 2
N = denitrification rate (gN/m -year)
• 2
N = total runoff and drainage losses (gN/m -year)
The fixation component, N , accounts for nitrogen fixation by leguminous
FX
crops and is estimated from the yield and nitrogen content of such crops
accounting for extra nitrogen fixed and contributed to the soil in the
•
forms of residues and root exudates. The fertilization component, N
FE
is based upon the assumed fertilization rate. The regional rainfall
n
component for northern Indiana is estimated at .3 gN/m -year (MRI, 1976).
Mineralization accounts for the breakdown of soil organic nitrogen com-
pounds and the resultant net release of inorganic nitrogen forms. This
is perhaps the most difficult of the input terms in the equation to
evaluate. Onishi, et al (1974), have equated this component to the
nitrogen content of the crop yield obtained when no fertilizer is
applied. A generalized nitrogen response curve for corn presented by
Lucas, et al (1977), indicates that yields without fertilization are
about 45 percent of the yields obtained under optimal fertilization.
The N,, term is assumed to equal the nitrogen equivalent of this corn
M
yield, less the precipitation input.
t
On the other side of equation (28), the yield component, N , is
estimated from crop yield and assumed nitrogen content. It includes
only the harvested product (not the residues, which are assumed to be
returned to the soil). The denitrification component, N , is estimated
198
-------
as a fraction of the calculated net nitrogen input rate:
ND= (NFX + NFE + NR + NM-VFD (29)
where
F = fraction of excess nitrogen which is denitrified
F is specified for each soil type; poorly drained soils have higher
values due to lower oxygen levels and lower leaching rates. The final
component, N , accounts for soluble nitrogen losses and is evaluated by
L
difference:
NL = NFX + NFE + \ + NM - NY - ND (30)
No distinctions are made between nitrogen losses in surface runoff and
subsurface drainage. Because of difficulties involved in estimating the
denitrified fraction, estimates of nitrogen losses obtained in this way
are probably better for relative comparisons of practices (e.g., percen-
tage differences) than as absolute levels.
Nitrogen is assumed to be transported conservatively to the down-
stream impoundment at the following rate:
LN=NL
where
L = impoundment nitrogen loading (gN/m -year)
199
-------
This scheme ignores particulate nitrogen losses attributed to soil
erosion. Sommers, et al (1975), measured total and exchangeable nitro-
gen in sediment from rainulator plots in the Black Creek Watershed. On
the average, only 1.2 percent and 5 percent of the total particulate
nitrogen was present as exchangeable ammonium in runoff from unferti-
lized and fertilized plots, respectively. Due to sedimentation and to
the relative stability of particulate organic nitrogen compounds, sedi-
ment nitrogen would not be expected to represent an important source of
available nitrogen (ammonium or nitrate) in downstream ecosystems, par-
ticularly when compared with soluble nitrogen sources calculated accord-
ing to the above scheme.
Dissolved Color
Estimates of dissolved color losses are required to provide partial
bases for estimating transparency and chlorophyll-a levels in downstream
impoundments. Of the components modeled in the watershed/impoundment
system, color is based upon the least amount of data and/or established
principles. The framework discussed below is quite theoretical and
should be considered tentative until data are located for calibration
and testing.
The presence of color in natural waters has often been attributed
to humic acids of soil origin (Wetzel, 1975). Estimates of dissolved
color in runoff are made here based upon computed sediment organic
matter content and assuming a linear adsorption isotherm between the
solid, organic matter phase and the dissolved color phase. Following
200
-------
the development for phosphorus, the average surface soil organic matter
content is computed from the baseline organic matter contents of the
various soil size fractions:
°0=XCL0CL+XSI0SI+XSA°SA
where
O = baseline organic matter content of surface soil (g/kg)
0 , 0 , 0 = baseline organic matter contents of clay, silt,
CL SX SA
and sand fractions (g/kg)
Following equation (16), the increase in surface organic matter content
due to tillage depth and crop residue addition is estimated from:
RES
40 =
where
Ao = change in surface soil organic matter content (g/kg)
RES = residue organic matter returned to soil surface (g/m2-year)
ZT = tillage depth (m)
p = soil density = 1300 kg/m3
D
K.. = an empirical parameter (year) ~
o
Inclusion of this term permits consideration of the enriching effects
of minimum tillage methods on surface soil organic matter levels. A
Kg value of .5 year has been assumed. For continuous corn, this gives
computed increases in •€> ranging from 64 percent to 275 percent when
201
-------
minimum tillage is used rather than conventional tillage in the various
Black Creek soils. Residue organic matter and residue phosphorus are
assumed to be related by:
RES = 500 RES (32)
o p
This assumes that crop residues are .2 percent phosphorus, a typical
value for corn (USEPA/USDA, 1975).
Assuming that the organic matter content of each size fraction is
increased proportionately, the average organic matter content of eroded
sediment is estimated as:
oE - fxE o°+xE o° + x° o° ) n + — i mi
° ~ (XCL °CL XSI °SI + XSA °SA) (1 + Qo> (33)
where
TT
O = average organic matter content of eroded sediment (g/kg)•
In order to estimate the concentration of dissolved color in surface
runoff, a linear adsorption isotherm is assumed:
COR = 2! (34)
where
CO = dissolved color in surface runoff (m"1);
R
Y = organic matter/color distribution coefficient (g/kgj/m"1
c
Dissolved color is expressed here in units of the visible light extinc-
tion coefficient, meters'1. Based upon the relationships discussed in
202
-------
the impoundment section, 1m"1 is approximately equivalent to 200 units
of Platinum-Cobalt color. Independent data for estimating the distri-
bution coefficient, y ' have not been located. For an assumed y value
c c
of 10 (g/kg)/m~ and typical field/watershed/impoundment characteristics,
computed values of CO are within the apparent range of observed color
R
values for impoundments (see Figure C-5, Methods for Predicting
Impoundment Water Quality). While this assumed value may be satisfac-
tory for a preliminary analysis, more data are needed to test the
assumed functional forms and parameter estimates for computing dissolved
color levels.
The average color concentration entering the downstream impound-
ment is computed from:
Cic =
where
C. = average dissolved color level in waters entering the
impoundment (m~l).
This assumes that the color content of subsurface drainage is negligible,
because it is in equilibrium with lower soil horizons which are rela-
tively deficient in organic matter.
Calibration of Models for Practice Evaluations
The models described above have been calibrated for use on three
soil/field types characteristic of the Black Creek Watershed, Indiana.
203
-------
Table B-3 summarizes the soil-specific parameter estimates and their
o
sources, most of which are self-explanatory. The q estimates for the
K.
various soil types are based upon the simulations performed by Woolhiser
(1976, 1977), as discussed previously. Literature values of F , the
fraction of excess nitrogen which is denitrified/ range from .25 (Onishi,
et al, 1974) to .80 (Huber, et al, 1977). Better drained soils would be
expected to have lower denitrification rates due to increased leaching
and increased soil aeration. F values of .5, .6, and .7 have been
assumed for the ridge, upland, and lowland soils, respectively.
The models have also been calibrated for evaluation of eleven modes
of farm operation on each of the three soil types. Parameter values are
summarized in Tables B-4, B-5, and B-6. Each mode of farm operation
is defined by a rotation, tillage method, and terracing scheme. Parame-
ter values represent the averages over the various crop rotations.
Instead of adjusting L (length of slope), P (practice factor) is
used to adjust the gross erosion rate when a terracing system is
employed. Installation of one terrace per field in practices 9 to 11
effectively reduces the length of slope by 1/2 and the gross erosion
rate by a factor of l/i/2~.
Estimates of cropping factors have been obtained from a generalized
table in Volume I of U.S. EPA/USDA (1975). According to Wischmeier and
Smith (1972), these values should be calculated for the Black Creek
region using the seasonal distributions of soil cover and rainfall
erosivity appropriate for the individual practices and for that region.
204
-------
Table B-3
FIELD/SOIL PARAMETER VALUES
SOIL TYPE
PARAMETER
Origin
Name
Texture
Hydrologic Soil Group
S
XCL
S
XSI
4
K
L
g
"n
p
po
o
o
*V
F
D
CD
0°
CL
°SI
0°
SA
EQUATION
-
-
(3)
(6)
*
(4)
(D
(2)
(2)
(16)
(16)
(16)
(14)
(24)
(29)
(23)
(30)
(30)
(30)
LOWLAND
lake plain
Hoytville
siltyclay
D
.43
.42
.15
.28
300.
.5
.166
.102
.049
.178
1.0
.7
.03
89.3
35.9
8.48
RIDGE
beach
Haskins
loam
B
.13
.44
.43
.37
300.
2.
.155
.036
.029
.064
1.0
.5
.03
88.1
14.2
3.32
UPLAND
glacial till
Morley
clayloam
C
.33
.44
.23
.43
300.
5.
.016
.011
.011
.127
.50
.6
.03
43.30
16.70
4.50
REFERENCE
a,b
a,b
a,b
d
a
a
a
f
e
a
b
b
b
g
a
g
e
b,c
b,c
b,c
a - Table 7.11, Sommers et al (1975)
b - Table 7.18, Sommers et al (1975)
c - Assuming Organic matter/total
nitrogen = 20, MRI, (1976)
d - SCS, USDA (1971)
e - Assumed value
f - SCS, USDA (1977) Figure 2.2 Lake
and Morrison.
g - Discussed in text.
205
-------
Table B-4
Practice Parameter Values for Lowland Soil
Parameter
Equation
Practice*
1 CC-CV
2 CC-CH
3 CC-NT
4 CB-CV
5 CB-CH
6 CB-NT
7 CBWM
8 CBWM-NT
9 CC-CV-T
10 CC-CH-T
11 CB-NT-T
* CC
CB
CBWM
CV
CH
NT
T =
P C
(1) (1)
1.00 .42
1.00 .19
1.00 .11
1.00 .43
1.00 .24
1.00 .18
1.00 .068
1.00 .043
0.71 .42
0.71 .19
0.71 .18
ZT
(17)
.18
.09
.025
.18
.09
.025
.0
.025
.18
.09
.025
Fp Fj^g RESp fR
(17) (26) (26) (14)
1.96 1.0 1.57 0
1.96 0.5 1.57 0
1.96 0.0 1.25 0
1.22 1.0 1.02 0
1.22 0.5 1.00 0
1.22 0.0 .90 0
1.10 0.14 .65 .20
1.10 0.0 .63 .20
1.96 1.0 1.66 0
1.96 0.5 1.66 0
1.22 0.0 .96 0
Continuous Corn
Corn/Bean Rotation
Corn/Be an/Whe at/Me adow
Conventional
Chisel Plow
No-Till
Terraced
Rotation
Tillage, fall plow
206
-------
Table B-5
Practice Parameter Values for Ridge Soil
Parameter
Equation
Practice
1
2
3
4
5
6
7
8
9
10
11
CC-CV
CC-CH
CC-NT
CB-CV
CB-CH
CB-NT
CBWM
CBWM-NT
CC-CV-T
CC-CH-T
CB-NT-T
P
(1)
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
.71
.71
.71
C
(1)
.42
.19
.11
.43
.24
.18
.068
.043
.42
.19
.18
ZT
(17)
.18
.09
.025
.18
.09
.025
.04
.025
.18
.09
.025
FP
(17)
1.96
1.96
1.96
1.22
1.22
1.22
1.10
1.10
1.96
1.96
1.22
FRES
(26)
1.0
0.5
0.0
1.0
0.5
0.0
.14
0.0
1.0
0.5
0.0
RESp
(26)
1.57
1.57
1.57
1.02
1.02
1.01
.66
.66
1.66
1.66
1.07
fR
(14)
0.
.35
.70
0.
.35
.70
.65
.80
0.
.35
.70
For the purposes of this project, however, regionalization would have
little influence on the relative or absolute evaluations of the prac-
tices considered.
z values of .18, .09, and .025 m have been assumed for conven-
tional (moldboard) plowing, chisel plowing, and no-till systems,
respectively. While chisel plows may penetrate soils to the same
depths as moldboard plows, the fact that they incorporate roughly one
half of the surface crop residues suggests that they cover one half of
207
-------
TABLE B-6
Practice Parameter Values for Upland Soil
Parameter
Equation
Practice
1
2
3
4
5
6
7
8
9
10
11
CC-CV
CC-CH
CC-NT
CB-CV
CB-CH
CB-NT
CBWM
CBWM-NT
CC-CV-T
CC-CH-T
CB-NT-T
P
(1)
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
.71
.71
.71
C
(1)
.42
.19
.11
.43
.24
.18
.068
.043
.42
.19
.18
ZT
(17)
.18
.09
.025
.18
.09
.025
.040
.025
.18
.09
.025
FP
(17)
2
2
2
1
1
1
1
1
2
2
1
.15
.15
.15
.34
.34
.34
.21
.21
.15
.15
.34
FRES
(26)
1
0
0
1
0
0
0
1
0
.0
.5
.0
.0
.5
.0
.16
•
.0
.5
.0
RESp
(26)
1.28
1.28
1.21
.81
.81
.80
.55
.55
1.35
1.35
.85
fR
(14)
0.
.17
.35
0.
.17
.35
.40
.43
0.
.17
.35
the surface area. Accordingly, an effective Z value of .09 m
is assumed for chisel plowing. For minimum tillage/ a value of .025 m
or 1 inch is assumed to represent the effects of natural mixing processes
in the soil (e.g., diffusion, earthworms, wind). Practice 7 consists of
a corn-bean-wheat-meadow rotation, with minimum tillage, except for the
fall preceding corn, in which conventional tillage is used. The average
Z value for this rotation has been selected so that Fp/Z is equal to
the average ratio over the four-year rotation (see equation (17)).
208
-------
The phosphorus in crop residues, RES , is estimated from the
assumed crop yields and residue phosphorus equivalents presented in
Table B-7. Conventional, chisel, and no-till systems are assumed to
incorporate 100 percent, 50 percent, and 0 percent of crop residues
into the soil after harvest, respectively. The values of F for
RES
Practices 7 and 8 have been selected so that computed values of RES
(1 - Fpgg) are equal to the respective averages of these products over
the four-year rotations (see equation (26)).
The runoff reduction factors, f , are estimated for each soil type
and practice using the methodology described previously (see Surface
Runoff and Percolation). Soil types are important in determining the
response of runoff rate to tillage methods. In soils subject to compac-
tion or with low internal permeability (e.g., lowland), minimum tillage
methods may not influence or actually cause increases in runoff rates
(Mannering, 1977). In well-drained soils (e.g., ridge) however, sub-
stantial runoff reduction can be expected when minimum tillage methods
are employed. The f values in Tables B-4, B-5, and B-6 have been esti-
mated assuming that the ridge, upland, and lowland soils respond well,
moderately and not at all, respectively, to reduced tillage.
The nitrogen budgets for all soil groups and practices are summa-
rized in Tables B-8, B-9, and B-10. The terms correspond to those in
equation (28). Nitrogen equivalents of crop yields have been estimated
using the coefficients in Table B-7. Using the methods described pre-
viously (see Soluble Nitrogen), the mineralization term is estimated at
4.2 gN/m -year. For a typical soil organic nitrogen content of 120 g/kg
209
-------
and a plow depth of seven inches, this mineralization rate corresponds
to a decay rate of about 1.5 percent per year, within the range of
reported values for soil humus, 1 to 4 percent per year (Buckman and Brady,
1960). This rate is assumed to be constant for all row crops and soil
types evaluated. In rotations, it is assumed to be zero during meadow
years. The final columns in Tables B-8 through B-10 represent the net
nitrogen inputs, which are used, along with F values, to estimate
soluble nitrogen losses in surface runoff and drainage.
Some evidence of "ground truth" can be developed by comparing the
computed unit emission rates of various components with those measured
in streams draining the Black Creek Watershed. Two automated stations
equipped for storm event sampling have been maintained on the watershed
by Purdue University since 1975. The characteristics of the drainage
Table B-7
Assumed Crop Parameters for Nitrogen Budget
and Residue Computations
Factor
Lbs. Yield P/bushel
Lbs. Yield N/bushel
Tons residue/bushel
Corn Bean Wheat
yield .16 .36 .28
yield .90 3.56 1.30
yield .030 .022 .030
Lbs. residue P/bushel yield .11 .089 .040
Haya
4.5
40.0
.18
.80
Hay yield units in tons instead" of bushels.
b USEPA/USDA Volume 1 (1975),
210
-------
Table B-8
Nitrogen Budgets for Lowland Soil
Practice
1
2
3
4
5
6
7
8
9
10
11
Term
(Equation (28)
2
) , (gN(m -year)
• • • • •
NFX NFE + NR + NM NY
CC-CV
CC-CH
CC-NT
CB-CV
CB-CH
CB-NT
CBWM
CBWM-NT
CC-CV- T
CC-CH-T
CB-NT-T
0.
0.
0.
8.
7.
6.
8.
8.
0.
0.
7.
0
0
0
38
60
82
91
91
0
0
21
17.
17.
19.
8.
8.
9.
4.
4.
17.
17.
9.
60
60
36
25
25
08
68
98
60
60
08
.30
.30
.30
.30
.30
.30
.30
.30
.30
.30
.30
4
4
4
4
4
4
3
3
4
4
4
.20
.20
.20
.20
.20
.20
.15
.15
.20
.20
.20
12
12
10
14
13
12
12
12
13
13
13
.87
.87
.30
.60
.82
.35
.68
.51
.56
.56
.09
N +N
D L
9.23
9.23
13.56
6.53
6.53
8.05
4.66
4.83
8.54
8.54
7.70
= Nitrogen fixation rate (gN/m -yr)
= Nitrogen fertilization rate (gN/m -yr)
2
= Nitrogen input in precipitation (gN/m -yr)
= Nitrogen input due to mineralization
of soil organic N (gN/m -yr)
= Nitrogen removal in crop yield (gN/m -yr)
= Net nitrogen excess = denitrification rate
2
loss rate (gN/m -yr)
211
-------
areas above these stations are presented and compared with the charac-
teristics of the entire watershed in Table B-ll. Nelson (1977) has pro-
vided preliminary data on the average flux rates of various components
at each station over each of two sampling years, 1975 and 1976. For
each station, year, and component, the contribution of septic tank
effluent estimated by Nelson has been subtracted from the reported total
flux.
Table B-9
Nitrogen Budget for Ridge Soil
Term (Equation (28)), (gN/m2-year)
Practice
1
2
3
4
5
6
7
8
9
10
11
cc-cv
CC-CH
CC-NT
CB-CV
CB-CH
CB-NT
CBWM
CBWM-NT
CC-CV-T
CC-CH-T
CB-NT-T
•
0.0
0.0
0.0
8.38
8.38
7.99
9.49
9.49
0.0
0.0
8.38
* *FE +
17.60
17.60
19.36
8.25
8.25
9.08
4.68
4.98
17.60
17.60
9.08
*R *
.30
.30
.30
.30
.30
.30
.30
.30
.30
.30
.30
•
4.20
4.20
4.20
4.20
4.20
4.20
3.15
3.15
4.20
4.20
4.20
•
^F ™
12.87
12.87
12.87
14.60
14.60
14.20
13.27
13.27
13.56
13.56
14.94
• •
9.23
9.23
10.99
6.53
6.53
7.37
4.35
4.35
8.54
8.54
7.02
212
-------
The ranges of observed fluxes are compared with the ranges of esti-
mated unit emission rates for various soil types and practices in Table
B-12. The soil types include lowland (lake plain), ridge (beach), and
upland (glacial till), while the practices include a corn-bean rotation
with conventional tillage (Practice 4 in Table B-4) and a corn-bean-wheat-
meadow rotation with minimum tillage, except for the year preceding corn
(Practice 7 in Table B-4). The former is the dominant form of row crop-
ping in the watershed. The two practices generally reflect the upper
Table B-10
Nitrogen Budgets for Upland Soil
2
Term (Equation (28)), (gN/m -year)
Practice
1 CC-CV
2 CC-CH
3 CC-NT
4 CB-CV
5 CB-CH
6 CB-NT
7 CBWM
8 CBWM-NT
9 CC-CV-T
10 CC-CH- T
11 CC-NT-T
V
0.0
0.0
0.0
6.42
6.42
5.84
7.87
7.87
0.0
0.0
6.23
*FE
13.75
13.75
15.13
6.33
6.33
6.96
3.72
3.92
13.75
13.75
6.96
\
.30
.30
.30
.30
.30
.30
.30
.30
.30
.30
.30
*M
4.20
4.20
4.20
4.20
4.20
4.20
3.15
3.15
4.20
4.20
4.20
*Y
10.40
10.40
9.88
11.33
11.33
10.75
10.97
10.97
11.09
11.09
11.48
N +N
D L
7.85
7.85
9.75
5.92
5.92
6.55
4.07
4.27
7.16
7.16
6.21
213
-------
Table B-ll
Characteristics of Drainage Areas Above Sampling Stations in
the Black Creek Watershed (Nelson, 1977)
Area (hectares)
Soil Types
Lake Plain and Beach
(Lowland and Ridge)
Glacial Till (Upland)
Land Use
Row Crop
Small Grain and Pasture
Woods
Urban
Site 2
942
71%
29%
63%
26%
8%
3%
Location
Site 6 Entire Watershed
714
26%
74%
40%
44%
4%
12%
4950
64%
36%
58%
31%
6%
5%
and lower limits, respectively, of the computed flux rates for the
various practices evaluated on each soil type.
As shown in Table B-12, year-to-year differences in the observed
fluxes are large. It would be impossible to obtain reliable estimates
of the long-term average fluxes of these components based only upon data
from two years of sampling. Because of this variability and because of
the distributions of land use, field characteristics, and cropping
practices in the watersheds, direct quantitative comparisons of the
observed and computed fluxes are not feasible. The ranges of observed
fluxes in Table B-12 correspond at least send-quantitatively to the
ranges of calculated unit emissions rates for various soil types and
practices.
214
-------
TABLE B-12. COMPARISONS OF OBSERVED AND ESTIMATED FLUXES OF VARIOUS COMPONENTS FROM THE BIACK CREEK WATERSHED
Observed
Estimated
to
Component
Losses
(Jcg/ha-yr)
Delivered
Sediment
Soluble
Phosphorus
Available
Sediment
Phosphorus
Total
Phosphorus
Soluble
Nitrogen
Total Flow
(m/yr)
Surface
Runoff
(m/yr)
Site 2d Site 6d
1975 1976 1975 1976
2126 636 3725 353
.20 .05 .32 .07
.35a .06a .19b .02b
.55* .lla .51b .09b
21.0 6.1 15.2 2.8
(.296) (.12) (.26) (.10)
-
Range
Min. Max.
353 3725
.05 .32
.02 .35
.09 .55
2.8 21.0
(.10) (.29)
-
Lowland Ridge Upland
CB-CV CBWM CB-CV CBWM CB-CV CBWM
1104 188 2459 458 7553 1301
.27 .32 .10 .15 .08 .14
.16 .03 .21 .06 .13 .04
.43 .35 .31 .21 .21 .18
19.6 14.0 32.8 21.7 23.7 16.2
.25 .25 .25 .25 .25 .25
.18 .14 .06 .02 .13 .07
Range
Min. Max.
188 7553 !
.08 .32
.03 .21
.18 .43
14.0 32.8
.25 .25
.07 .18
a Assuming available sediment P/Total Sediment P = .069 ratio for average soil type in subwatershed
b Assuming available sediment P/Total Sediment P = .044 ratio for average soil type in subwatershed
c Septic tank contributions estimated by Nelson (1977) have been subtracted from the total measured loadings.
d For site characteristics, see Table 11.
e Total flow measurements may not reflect all of groundwater contributions
f CB-CV = corn-bean rotation with conventional tillage; CBWM = corn-bean-wheat-meadow rotation with minimum
tillage except year preceeding corn.
-------
The range of computed soluble nitrogen export (14 - 32.8 kg/ha-yr)
appears to be somewhat high, compared with the observed range (2.8 to 21
kg/ha-yr). The extent to which all of the groundwater contributions are
reflected in the reported measurements is unclear however, since some
of the groundwater contributions may emerge further downstream in Black
Creek or in the Maumee River. Since groundwater is an important trans-
port medium for nitrate, the observed nitrogen export values may be
biased on the low side. Alternatively, the assumed denitrification
rates could be under-estimated, or soil nitrogen mineralization rates,
over-estimated.
While the comparisons in Table B-12 do not "verify" the methodology
or calibration, they suggest, minimally, that the estimates are not off
by more than an order of magnitude.
REFERENCES, APPENDIX B
Brigham, W. U. "Phosphorus in the Aquatic Environment." A Report to the
Subcommittee on Fertilizers, Illinois Task Force on Agriculture, Non-
Point Pollution, Urbana, Illinois, March 1977.
Buckman, H. O. and N. C. Brady. The Nature and Properties of Soils. The
MacMillan Company, New York 1960.
Christensen, R. G. and C. D. Wilson ed. Best Management Practices for Non-
Point Source Pollution Control. EPA-905/9-76-005, U.S. EPA, Region 5,
Chicago, November 1976.
Foth, H. D. and L. M. Turk. Fundamentals of Soil Science. John Wiley and
Sons, Fifth Edition 1972.
Harmeson, R. H., F. W. Sollo and T. E. Larson. "The Nitrate Situation in
Illinois." Journal of the American Water Works Association, Vol. 63, No.
5, May 1971, pp. 303-310.
216
-------
Huber, D. M., H. L. Warren, D. W. Nelson and C. Y. Tsai. "Nitrification In-
hibitors - New Tools for Food Production." Bio Science, Vol. 27, No. 8,
August 1977, pp. 523-549.
Jones, L. A., N. E. Smeck and L. P. Wilding. "Quality of Water Discharged
from Three Small Agronomic Watersheds in the Maumee River Basin."
Journal of Environmental Quality, Vol. 6, No. 3, 1977.
Kilner, "Enrichment of Clay and Surface Erosion." Advances in Agronomy,
1960.
Lake, J. and J. Morrison, eds. Environmental Impact of Land Use on Water
Quality. Progress Report - Black Creek Project, Allen County, Indiana,
Allen County Soil and Water Conservation District, EPA-905/9-75-006,
November 1975.
Lucas, R. E., J. B. Holtman and L. J. Connor. "Soil Carbon Dynamics and
Cropping Practices." From Lockeretz, W. ed. Agriculture and Energy,
Academic Press 1977.
Massey, H. F., M. L. Jackson and O. E. Hayes. "Fertility Erosion on Two
Wisconsin Soils." Agronomy Journal, Vol. 45, 1953, pp. 543-547.
Midwest Research Institute. "Loading Functions for Assessment of Water Pollu-
tion from Non-Point Sources." EPA-600/2-76-151, U.S. EPA, ORD, Washing-
ton D. C., May 1976.
Nelson, D. W. Draft Tables of Measured Nutrient and Sediment Export Data
from Black Creek Watershed, 1975-1976. Personal Communications, Purdue
University, Department of Agronomy, November 1977.
Olness, A. and D. L. Rausch. "Callahan Reservoir: III Bottom Sediment -
Water-Phosphorus Relationships." Transactions of the ASAE, Vol. 20, No. 2,
1977, pp. 291-300.
Omernik, J. M. "The Influence of Land Use on Stream Nutrient Levels." EPA-
600/3-76-014, Environmental Research Laboratory, Office of Research and
Development, Corvallis Environmental Research Laboratory, January 1976.
Onishi, H., A. S. Narayanan, T. Takayama and E. R. Swanson. "Economic Evalua-
tion of the Effect of Selected Crop Practices on Nonagricultural Uses of
Water." University of Illinois at Urbana-Champaign, Water Resources
Center, UJLU-WRC-74-0079, Research Report No. 79, March 1974.
Porter, K. S. Nitrogen and Phosphorus - Food Production, Waste and Environ-
ment. Ann Arbor Science, Michigan, 1975.
Mannering, J. V. Personal Communication, Agronomy Department, Purdue Univer-
sity, December 1977.
217
-------
Rausch, D. L. and H. G. Heinemann. "Controlling Reservoir Trap Efficiency."
Transactions of ASAE, Vol. 18, 1975, pp. 1185-2113.
Romkens, M. J. M. and D. W. Nelson. "Phosphorus Relationships in Runoff from
Fertilized Soils." Journal of Environmental Quality, Vol. 3, No. 1, 1974,
pp. 10-13.
Romkens, M. J. M., D. W. Nelson and J. V. Mannering. "Nitrogen and Phospho-
rus Composition of Surface Runoff as Affected by Tillage Method." Journal
of Environmental Quality, Vol. 2, No. 2, 1973, pp. 292-295.
Soiraners, L. E. et al. "Section 7 - Water Quality Monitoring in Black Creek
Watershed." Environmental Impact on Land Use and Water Quality, Lake
and Morrison eds., 1975.
Stall, J. B. "Effects of Sediment on Water Quality." Journal of Environmen-
tal Quality, Vol. 1, No. 4, 1972, pp. 353-360.
Stoltenberg, N. L. and J. L. White. "Selective Loss of Plant Nutrients by
Erosion." Proceedings of the Soil Science Society of America, 1953, pp.
406-410.
Tanjii, K. K., M. Fried and R. M. Van de Pul. "A Steady - State Conceptual
Nitrogen Model for Estimating Nitrogen Emissions from Cropped Lands."
Journal of Environmental Quality, Vol. 6, No. 2, 1977, pp. 155-159.
Taylor, A. W. "Phosphorus and Water Pollution." Journal of Soil and Water
Conservation, Vol. 22, 1967, pp. 228-231.
Taylor, A. W. and H. M. Kunishi. "Phosphate Equilibria on Stream Sediment
and Soil in a Watershed Draining an Agricultural Region." Journal of
Agricultural and Food Chemistry, Vol. 19, No. 5, 1971, pp. 827-831.
Timmons, D. R., R. E. Burwell and R. F. Holt. "Nitrogen and Phosphorus in
Surface Runoff from Agricultural Land as Influenced by Placement of
Broadcast Fertilizer." Water Resources Research, Vol. 9, No. 3, June
1973, pp. 658-667.
Timmons, D. R., R. F. Holt and J. J. Latterell. "Leaching of Crop Residues
as a Source of Nutrients in Surface Runoff Water." Water Resources
Research, Vol. 6, No. 3, October 1970, pp. 1367-1375.
Timmons, D. R., R. E. Brunwell and R. F. Holt. "Loss of Crop Nutrients
Through Runoff." Minn. Science, Vol. 24, No. 4, 1968, pp. 16-18.
U.S. Department of Agriculture. Present and Prospective Technology for Pre-
dicting Sediment Yields and Sources. Proceedings of Sediment Yield Work-
shop, USDA Soil Lab., Oxford, Miss., November 28-30, 1972, ARS-S-40,
ARS, USDA, June 1975.
218
-------
U.S. Department of Agriculture, Soil Conservation Service. "National Engi-
neering Handbook, Section 4, Hydrology." U.S. Government Printiny Office,
Washington D. C., 1971.
U.S. Department of Agriculture, Soil Conservation Service. "Technical Guide"
Section III - A-2.5, Fort Wayne Field Office, Indiana, August 1977.
U.S. Environmental Protection Agency and U.S. Department of Agriculture.
Control of Water Pollution from Cropland, Vol. I - A Manual for Guideline
Development. EPA-600/2-75-026a, November 1975.
U.S. Environmental Protection Agency and U.S. Department of Agriculture.
Control of Water Pollution from Cropland, Vol. II - An Overview, EPA-600/
2-75-026b, June 1976.
Vanoni, V. A. ed. Sedimentation Engineering. ASCE, New York 1975, 745 pp.
Wischmeier, W. H, "Use and Misuse of the Universal Soil Loss Equation."
Journal of Soil and Water Conservation, Vol. 31, January-February 1976,
pp. 5-9.
Wischmeier, W. H. and D. D. Smith. Predicting Rainfall - Erosion Losses from
Cropland East of the Rocky Mountains. ARS, U.S. Department of Agricul-
ture, Agriculture Handbook No. 282, 1972.
Woolhiser, D. A. "Hydrologic Aspects of Non-Point Pollution." USEPA/USDA,
1976.
219
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Appendix G
Methods for Predicting Impoundment Water Quality
Introduction
The models described below have been developed for use in assessing
the impacts of agricultural practices on impoundment water quality. They
are of an empirical nature and are designed to predict steady-state con-
ditions in impoundments with regard to the following water quality com-
ponents :
(1) sediment concentrations and trapping rates;
(2) total phosphorus concentrations and trapping rates;
(3) total nitrogen concentrations and trapping rates;
(4) mean summer, Secchi Disc transparencies; and
(5) mean summer, epilimnetic chlorophyll-a concentrations.
Models are formulated for each of the above components based upon theo-
retical considerations and the results of previous modeling efforts.
When possible, calibration is achieved through a formal parameter esti-
mation exercise, using an appropriate data base. Models are "verified"
based upon analyses of residuals, tests for parameter stability and/or
use of an independent data base. In other cases, parameter estimates
are derived from measurements or experiments described in the literature
and are therefore more subjective. In applying these models, sensitivity
analyses will help to identify which of the parameter estimates require
more detailed study and evaluation.
220
-------
The methods can be used to assess the sensitivities of the above
water quality components to annual average input rates, or loadings,
of the following substances:
(1) water;
(2) sediment (sand, silt, and clay);
(3) phosphorus (total soluble and extractable particulate);
(4) nitrogen; and
(5) color (dissolved).
Additional independent variables of importance include:
(6) mean depth; and
(7) impoundment type (reservoir vs. natural lake).
A variety of other morphometric, hydrologic, and regional factors have
also been evaluated as possible independent variables, but have been
found to be of relatively minor importance, at least within the three-
state region in which the models have been calibrated (Ohio, Indiana,
and Illinois). Due to the empirical nature of the models, use outside
of this region is not suggested, unless recalibration can be achieved
using an appropriate data base. Some submodels and parameter estimates
are more theoretically based than others and may be more transferable
to other regions. The pathways in the impoundment water quality analy-
sis are summarized in Figure C-l.
Data Base
The primary data base used in this effort is compiled in the attached
tables. The EPA's National Eutrophication Survey (1976) has provided
221
-------
LOADINGS
TRAPPING/
DECAY RATES
OUTFLOW/
EPILIMNETIC
CONCENTRATIONS
Color
•*• Color
to
to
to
Sediment
Phosphorus
Nitrogen
Sediment
Phosphorus
Nitrogen
Color
-*• Suspended
Solids
IMPOUNDMENT MORPHOMETRlC
AND HYDROLOGIC CHARACTERISTICS
Transparency
Chlorophyll-a
Concentration
Figure C-l. Pathways in Predicting Impoundment Water Quality
-------
the following types of information for each of fifty impoundments in
the Ohio-Indiana-Illinois region:
(1) location (state, latitude, longitude);
(2) hydrology (average outflow rate);
(3) morphometry (volume, surface area, drainage area, mean depth,
maximum depth);
(4) total nitrogen and total phosphorous budgets (annual input,
output, and retention rates); and
(5) trophic state indicators (mean summer chlorophyll-a and
transparency).
The National Eutrophication Survey (NES) included a total of 75
impoundments in this region. The remaining 25 have been excluded from
the study for one or more of the following reasons:
(1) nutrient and/or hydrologic budgets were either not determined
or acknowledged by the NES as uncertain due to incomplete
tributary and point source sampling program designs;
(2) mean depths were less than one meter;
(3) mean hydraulic residence times were less than 3 days;
(4) surface overflow rates were greater than 150 m/year; and/or
(5) other, unusual factors may have influenced nutrient dynamics;
(e.g., Lake Sangchris Illinois has not been included because
it is mixed via power plant cooling operations).
An additional data set of 20 impoundments has been compiled from those
rejected above and from NES impoundments in Iowa. These data, considered
of lower quality, have been used as a partial basis for verification of
the chlorophyll model.
Sedimentation rate data for fifteen of these impoundments have also
been obtained primarily from the USDA (1969). Additional sources of water
223
-------
quality data, used for calibrating the optical component submodels, in-
clude the U.S. Army Corps of Engineers (1977), Illinois State Water Sur-
vey (1977), and the Indiana State Board of Health (1976).
Sedimentation
Curves developed empirically by Bruyne (1953) are used to predict
the sediment trapping efficiency of an impoundment as a function of mean
hydraulic residence time, T (years). The latter is equivalent to Bruyne's
"Capacity to Average Annual Inflow Ratio." The trap efficiency, R is
s
defined as the fraction of influent sediment which is deposited within
the impoundment:
R = 1 - os
s
L.
IS
where
Rg = trapping efficiency (dimensionless) ;
os = average sediment outflow rate (kg/m2-yr) ;
L. = average sediment inflow rate (kg/m -yr) .
Bruyne's original "envelope curves" characterizing the R vs. T relation-
S
ship were based upon analysis of data from 38 impoundments. These curves
are shown in Figure C-2, along with the following algebraic form, which is
approximately equivalent :
K T
R -
8
8
224
-------
where
T = mean hydraulic residence time (years)
Kg = an empirical sediment decay rate parameter (year)
-1
This form essentially represents the trapping process as a first order
decay reaction in a completely mixed system, characterized by a decay
coefficient, K . Figure 2 shows that Bruyne's median curve is approxi-
s
mately equivalent to a K value of 68 year or about .20 days . Agree-
ment is reasonable for impoundments with T values greater than .003 years.
1.0
Bruyne's Curves
Upper Envelope
Median
Lower Envelope
RS=KST/(1
Ks=120 year"1
Kss 68 year"1
Ks= 50 year"1
.001 .002
.005 .01 .02 .05 .10 .20 .50
T = Mean Hydraulic Residence Time (years)
Figure C-2. Sediment Trapping Efficiency Relationships
225
-------
From a theoretical point of view, a better form would represent
sediment trapping as a first order settling process, in which case the
decay coefficient would represent an effective settling velocity (m/year),
and the independent variable would be surface overflow rate (m/year).
The effects of seasonal temperature variations, flow variations, non-
ideal settling behavior, particle size distribution, and particle size
changes due to flocculation would render it difficult, however, to select
an appropriate velocity based upon Stoke"s Law. Bruyne's approach is
more approrpiate for use in this context because it has been empirically
verified.
Bruyne's model is modified here to account for the variation of
trap efficiency with sediment texture or particle size. Smaller parti-
cles are less efficiently trapped within an impoundment due to their
lower settling velocities. This results in the clay fraction of suspen-
ded solids in impoundment outflows being higher than those in impound-
ment inflows. Rausch and Heinemann (1975) attributed much of the
observed variation in the trapping efficiency of Callahan Reservoir to
variations in the clay fraction of entering sediment.
This effect is included by using a different decay rate parameter
for each sediment texture class (clay, silt, and sand). Since clay
and silt generally comprise the bulk of sediment loadings, decay rate
parameters for clay (50 year"1) and silt (120 year"1) have been selected
to correspond with Bruyne's lower and upper envelope curves in Figure C-2,
respectively. Essentially all influent sand would be expected to be
226
-------
trapped. Accordingly, an arbitrarily high value of 8000 year"1
has been assumed for the sand decay rate.
Based upon mass balance considerations, the average suspended
solids concentration in an impoundment outflow can be estimated from:
cos = ci
C. = L. /Q //n
is is *s <4J
where,
C = outflow suspended solids concentration (kg/m );
OS
C. = inflow suspended solids concentration (kg/m );
JLS
Q = surface overflow rate (m/year).
s
Both the trapping rates and suspended solids concentrations are deter-
mined as the sum of the respective values for all texture classes.
Phosphorus Trapping and Concentration
Phosphorus is considered an important water quality variable inso-
far as it may control the growth of phytoplankton in an impoundment.
The models for chlorophyll concentration and transparency developed in
subsequent sections rely upon predictions of Cop, the average outflow
total phosophorus concentration. CQp estimates are developed from
average inflow phosphorus concentrations and a retention model. As in
the case of sediments, the retention model predicts the fraction of in-
fluent phosphorus which is trapped in the lake sediments as a result of
227
-------
various physical, chemical, and biological reactions occurring in the
water column. (Dillon, 1974). A retention model is formulated and
calibrated for Cornbelt impoundments below.
A previous analysis of data from north central and northeastern
U.S. impoundments (Walker, 1977) suggested that a model of the follow-
ing form would be appropriate for predicting phosphorus retention co-
efficients:
_ R =
R
p C. 1 + K T
IP P
Kp =
where,
R = retention coefficient for total phosphorus (dimensionless)
P
C = average outflow total P concentration (g/m )
op
C. = average inflow total P concentration (g/m )
K = effective first order decay coefficient for total P (year)
Z = mean depth (m)
Q = surface overflow rate = Z/T (m/year)
s
bg,bi,b2 = empirical parameters.
This essentially represents phosphorus trapping as a first order decay
process in a mixed system, with the decay rate allowed to vary with Qg
and z according to equation (6). The latter dependences were included
to allow for possible effects of incomplete mixing or other factors
228
-------
related to depth and overflow rate. Best estimates of the empirical
parameters b0, bj, and b2 for lakes north of 42° latitude suggested the
following model:
Kp = -82 -2- '•55 = .82 T~'55 (7)
- R "
4
1 + .82 T
Equation (8) explained 78 percent of the variance in the reported re-
tention coefficient data for 105 impoundments (Walker, 1977). Similar
models have been developed independently by Larsen and Mercier (1975)
and by Vollenweider (1976) , for lakes in the same latitude range.
Figure C-3 demonstrates that the trapping efficiencies of most of
the impoundments in the Ohio-Indiana-Illinois region are considerably
higher than those predicted by equation (8). Accordingly, a more
general form of the above model has been tested for these impoundments;
! (9)
1 - R = =
P ai S2 a3
1+aOQs Z Cip
An equivalent form of equation (9) is appropriate for a log-linear
regression analysis:
R a. a_ a
• •„«.lz SP
p
a =1.986
o
229
-------
10
U)
o
.9
_o
Ifc-
t
.7
c .5
•>
€»
(E
M .4
3
M
o
£
2 -2
o
it
O. .1
Gt
1 1
A A
A A
.3 -
.824T-484
P~H-.824T'4M
• -
Model for Northcentral
and Northeastern Impoundments
Ohio, Indiana f• Natural Lakes
and Illinois U Reservoirs
J_L
1
J L
1
J—L_L
4.0 6jO 8.0 10.
.02 .04 .06 .08 .10 .20 .40 .60 .80 1.0 2.0
T = Mean Hydraulic Residence Time (years)
Figure C-3. Relationship between Total Phosphorus Retention Coefficient and Mean Hydraulic Residence Tijne
-------
a1 = -.309 ± .101
a2 = .805 ± .240
a3 = .621 ± .185
All coefficients are significant at the 95 percent confidence level,
but equation (10) explains only 36 percent of the variance in log
(R /(1-R )). This is a low level of predictive ability, relative to
that demonstrated by equation (8) for northern lakes. This suggests
that other factors may be controlling phosphorus trapping in Corn Belt
impoundments and/or that these data are of poor quality relative to
those used in developing equation (8). The latter explanation is con-
sidered less likely, because the data bases for both models have been
derived primarily from the NES, in which consistent sampling program
designs and data handling procedures were maintained.
In order to permit an assessment of the possible effects of sedi-
mentation on phosphorus trapping, sedimentation rates for 15 of the
NES lakes have been obtained from a national data summary published
by the USDA (1969) and from local studies by the Illinois State Water
Survey (1977b) and the Army Corps of Engineers (1970). Effects of sedi-
mentation have been evaluated with a modified form of equation (10):
a, a a_ a_
*' %P \ 4
2
where, S = sedimentation rate (kg/m -lake surface-year)
a = .246
o
231
-------
a = -.491 ± .180
a = .280 ± .328
a = .647 ± .309
a = 1.095 ± .302
For the fifteen impoundments tested, equation (11) explains 76 percent
of the variance in log.,0(R /(1-R )), a marked improvement over the
performance of equation (10). All of the coefficients are significantly
different from zero, with the exception of the depth exponent, a_.
The relatively narrow range of mean depths in this subsample of lakes
(1.2-5 meters) may have been responsible, in part, for this lack of
significance.
The apparent importance of sedimentation rate as a factor influ-
encing phosphorus trapping is indicated by the size of a relative to
the other exponents. Multicollinearity among the four factors tested
renders it difficult to establish the relative magnitude of the various
coefficients with much confidence, however. The correlation matrix
of parameter estimates is presented below:
1.00
.31 1.00
a3 .37 .19 1.00
-.76 -.51 -.34 1.00
232
-------
The sedimentation coefficient, a , is most significantly corre-
lated with the overflow rate exponent, a (r = -.76). This is attri-
buted to S and Q both being dependent upon the ratio of drainage
T S
area to surface area. The failure of Q to explain much of the reten-
tion coefficient variance in the larger data set indicates that S
does have significant predictive capability, although the relative
magnitudes of the coefficients a1 and a. are somewhat difficult to
1 4
determine from these data.
The measured sedimentation rates employed in the above regression
analysis primarily reflect external loadings of sediment from the
respective watersheds, as opposed to sediment generated within the
impoundments as a result of primary production and chemical precipita-
2
tion. The reported S values range from 3 to 71 kg/m year. The maxi-
mum rate of net primary production for temperate, eutrophic lakes
reported in a data summary compiled by Wetzel (1975) corresponds to
about 1.5 kg organic matter/m -year. Due to decay processes and re-
spiration in the food chain, a small fraction of net production is
usually sedimented. Estimates for Lawrence and Mirror Lakes are on
the order of seven percent (Wetzel, 1975). Precipitation of calcium
carbonate would also contribute to measured sedimentation rates.
Alkalinity changes, induced by photosynthetic removal of CO2/ are on
the order of .5 kg CaCO /m2 year for eutrophic systems (Vollenweider
(1968), Otsuki, et al. (1974)). Thus the reported sedimentation rates
are assumed to result primarily from erosion in the respective water-
sheds.
233
-------
Modifications of the reported phosphorus retention coefficient
data have been made in order to improve the reliability of the para-
meter estimates. The NES phosphorus loading estimates were based upon
monthly grab samples of lake tributaries. It is doubtful that these
estimates reflect loadings of particulate phosphorus entering during
storm events. In a study of the NES Non-Point Source Watersheds,
Omernik (1976) reported that an average of 41 percent of the total
phosphorus export from 96 agricultural watersheds (80 of which were in
the Corn Belt region) was in the ortho-phosphorus form. This is in
contrast with data derived from continuous flow-weighted composite
sampling, which typically indicate less than 10 percent ortho-phosphorus
(Nelson, et al., 1976). An attempt to account for unsampled, extract-
able, particulate phosphorus loading has been made for each of the
fifteen lakes according to the following:
L1 = L + L Y (12)
p p s ps
L
R' = 1 - (1 - R ) -2- (13)
P P L.
L =S(l+-i-) (14)
s t 68T
where,
2
L ,L' = reported and corrected phosphorus loadings (g/m -year)
P P
R ,R' = reported and corrected phosphorus retention coefficients
p p (dimensionless)
234
-------
2
L = estimated external sediment loading (kg/m -year)
s
Y = assumed extractable phosphorus content of entering
sediment = .08 g/kg.
Equation (14) estimates the external sediment loading, L , from the
S
reported trapping rate S , by employing Bruyne's trapping curve (Fig-
ure C-2). The assumed value of Y is based upon measurements of
ps
extractable phosphorus contents of sediment measured in Black Creek
rainulator studies (Sommers, et al., 1973) and in four Missouri Valley
agricultural watersheds (Schumann, et al, 1973). This effort to correct
the phosphorus loadings and retention coefficients reported by the NES
is admittedly approximate, but is considered preferable to using the
reported values directly. The reported and corrected loadings and reten-
tion coefficients are listed in attached tables. Using the corrected
retention coefficient data, the parameters of equation (11) have been
re-estimated:
a0 = .419
ax = -.757 ± .127
a2 = .236 ± .222
a3 = .077 ± .207
ai» = 1-175 ± .205
Since 33, the exponent for C. , is not signi'ficantly different from zero,
it has been excluded and the remaining parameters, re-estimated:
a0 = .377
ai = -.779 ± .109
a2 = -222 ± .211
235
-------
33 = 0
ait = 1.201 ± .186
With these parameter values, equation (11) explains 86 percent of the
variance in the "corrected" log.. (R /(1-R )) values and 77 percent of
the variance in R , with a standard error of .09. Despite its low
P
significance level, the depth coefficient (a,,) has been allowed to remain
because this lack of significance may be attributed to the relatively
narrow range of mean depths in the data base (1.2-5.0 meters).
The apparent importance of sedimentation rate as a factor influ-
encing phosphorus retention is partially supported by theory and
independent experimental evidence. The adsorption of phosphorus by
soils and sediments has been studied extensively and is considered to
involve primarily the adsorption of iron and aluminum phosphate compounds
to clay particle surfaces (Syers, et al, 1972). Kunishi, et al, (1972)
have observed this adsorption process to be partially irreversible.
Under the anaerobic conditions typical of lake bottom sediments, iron
phosphate compounds are much more soluble and equilibrium may favor the
release of phosphorus into the water column. The rate of release may
be severely limited, however, by kinetics (e.g., diffusion rates).
Apatite formulation in calcareous sediments represents a permanent
phosphorus sink (Stumm and Leckie, 1970). The empirical evidence pre-
sented above suggests that external sediment loadings do contribute to
net phosphorus trapping efficiency. Thus, release of dissolved phos-
phorus from these lake bottoms may be small relative to adsorption/
sedimentation rates despite the fact that dissolved oxygen concentra-
236
-------
tions less than 1 g/m were detected by the NES in the bottom waters
of seven out of the fifteen impoundments. An important implication
is that particulate phosphorus loadings may have little effect on aver-
age epilimnetic or outflow phosphorus concentrations in these types of
impoundments. In fact, reductions in soil erosion could conceivably
result in reductions in phosphorus trapping efficiencies and subse-
quent increases in average epilimnetic phosphorus levels. These relation-
ships may not hold true for impoundments with greater mean depths, which
would have more pronounced stratification and greater potential for
phosphorus recycling through anaerobic bottom waters.
Additional theoretical interpretations of these results are
possible with reference to the "settling velocity" model proposed by
Vollenweider (1969) and Chapra and Tarapchak (1976) to predict phos-
phorus retention coefficients:
1 - % ' ^ - rrf/5;
where,
U = effective settling velocity for total phosphorus (m/yr).
P
Vollenweider (1969) showed that a U value of approximately 10 m/yr
was appropriate for a sample of northern temperate lakes. Comparing
this formulation with equation (11) and the last set of regression
coefficients shows that the settling velocity for these 15 Corn Belt
impoundments can be estimated from:
237
-------
UP -
« .377 Q .231Z.222 1.201 (17)
s t
The relative magnitudes of the exponents suggest a dominant influence
of S , the sedimentation rate.
While measured sedimentation rates were available for only 15 of
the 50 impoundments included in this study, further indirect evidence
can be presented for the effect of S on phosphorus settling velocity.
One would expect lakes with large percentages of their drainage areas
impounded upstream to have relatively low sedimentation rates, because
of sediment trapping upstream. This, in turn, should result in lower
phosphorus settling velocities, according to equation (17). Five such
lakes could be identified within the original set of fifty. Table c-1
compares the measured phosphorus settling velocities (equation (18)) of
these lakes with velocities measured in the lakes immediately upstream.
These data indicate a consistent decreasing trend in phosphorus set-
tling velocity moving downstream in each watershed. For example,
Witmer flows into Westler, and Westler, in turn, into Dallas. The U
P
values for these lakes are 16.0, 10.2, and 2.0 m/year, respectively.
In addition, James Lake, the only lake in the data set with a reportedly
negative phosphorus retention coefficient, has a watershed, 87 percent
of which is impounded upstream. While alternative explanations are
possible, these data are at least consistent with the theory that sedi-
mentation rates partially control phosphorus trapping in these impound-
ments .
238
-------
Equation (16) is considered rather tenuous for use as a predictive
tool, because of its relatively small data base, parameter collinearity
and rather empirical form. In applying the model to evaluate the water
quality impacts of agricultural practices, a minimum value of 3 kg/m2-year
is assumed for Sfc, since the relationship between phosphorous settling
velocity and sedimentation rate has not been examined below this
Sfc value- Compilation of additional data from other areas of the
country and testing some more theoretically formulated models would be
worthwhile in the interest of further defining the relationships among
Table c-1
Phosphorus Settling Velocities in Lakes and
Reservoirs with Partially Impounded Watersheds
Lake or
Reservoir*
Witmer
Westler
Dallas
Webster
James Lake
Olin
Oliver
Shelbyville
Carlyle
NES
Number
349
346
326
345
330
338
339
315
297
Percent of Water-
shed Impounded
0
96
96
0
87
0
55
0
39
U **
P
(m/yr)
16.01
10.19
1.98
16.15
- 1.09
40.01
6.75
26.96
9.59
* Grouped moving downstream in each watershed (e.g. Witmer flows into
Westler and, in turn, into Dallas)
**
p
£3jT- = effective phosphorus settling velocity.
239
-------
phosphorus retention, sedimentation rate, hydrology, and impoundment
morphometry.
Average outflow phosphorous concentrations are estimated from the
average inflow concentrations and estimated retention coefficients
according to the following:
CoP = CiP
where,
C = average outflow total phosphorus concentration (g/M )
op
The outflow concentration is a good indicator of typical lake con-
*• _^
centrations. A regression analysis of data from the 23 natural lakes
in the data set suggests the following relationship:
C = .935 C -1-062 (19)
mp op
2
{R = .921, SEE = .136}*
A similar analysis of data from 27 reservoiis yields the following:
C = .605 C *887
mp op
{R2 = .702, SEE = .139}*
* Coefficient of determination and standard error of estimate,
respectively, referring to log... (C ).
10 mp
240
-------
where,
C = spatial and temporal median, summer total phosphorus concen-
tration in the impoundment (g/m ).
Note that the slope of the relationship is less for reservoirs as com-
pared with natural lakes. This could be due to differences in hydro-
dynamics, particularly effects of bottom-water withdrawals from some
reservoirs.
Nitrogen Trapping and Concentration
Nitrogen is considered an important water quality variable for
two primary reasons. High nitrate levels are of concern with regard to
drinking water quality/ because of the possible toxicity. Secondly,
supplies of fixed nitrogen are also required to support most types of
algal growth. The development of a predictive model for nitrogen concen-
tration is analagous to that described above in the case of phosphorus.
The impoundments sampled by the NES in the region appear to be
significantly less efficient in trapping nitrogen than in trapping
phosphorus, as indicated by the following regression equations:
R = -.032 + .618 R {R2 = .40, SEE = .17} (21)
n p
U = 945 U'506 {R = *25' SEE = '51>* (22)
n ' p
log statistics.
241
-------
One explanation for this behavior is that nitrogen is supplied to these
impoundments well in excess of phosphorus, relative to biological require-
ments. The ratio of geometric mean nitrogen to phosphorus loadings is
24, about three times that typical of algal biomass. Limiting nutrient
bioassay studies conducted by the NES also indicate that the algae in
most of these impoundments are phosphorus, as opposed to nitrogen-limited,
given sufficient light.
Fixation of nitrogen by blue-green algae might also be responsible,
in part, for relatively low nitrogen retention efficiency. This phenomenon
is probably not very important in the context of the total nitrogen budgets,
however, since reported direct measurements of N fixation in aquatic sys-
2
terns range from 0 to .4 gN/m - year (Wetzel, 1975), whereas reported
external nitrogen loadings for the fifty impoundments examined here
2 2
average 103 gN/m - year and range from 3.3 to 597 gN/m - year. The
presence of high nitrate concentrations would also tend to suppress
nitrogen fixation activity (Wetzel, 1975).
Another factor possibly tending to decrease nitrogen trapping
efficiency is that nitrate nitrogen is not significantly adsorbed by
sediments. This would tend to reduce the importance of sedimentation
as a nitrogen removal mechanism, as compared with phosphorus, but may
be offset, to some degree, by denitrification. This has been tested
empirically by performing a regression analysis of the nitrogen retention
data, using a model analogous to that employed for phosphorus (Equation 11):
Rn n cl C2 „ C3 0 C4 (23)
F-=CoQs Z Cin St
n
242
-------
c = 1.928
o
= -1.155 ± .395
.262 ± .770
= -.625 ± .716
c,, = .447 ± .672
4
{R = .56, SEE = .565}
The sedimentation rate exponent, c , is not significantly different from
zero, suggesting that nitrogen retention is not as strongly linked to
sedimentation as is phosphorus retention. Similar conclusions are
reached when alternative forms of this model are estimated, deleting
the other insignificant parameters (c and c ).
The parameters of equation 23 have been re-estimated, setting
c = 0 and using a data base of 43 impoundments:*
c = .223
o
c = -.445 ± .092
= .351 ± .200
= .862 ± .299
= 0
{R2 = .455, SEE = .343}
* The retention coefficients of the seven impoundments with reported
value less than zero have been excluded in order to permit the regression
analyses to be performed on a logarithmic scale.
243
-------
Analysis of residuals from this model suggests that R values are under-
predicted slightly (by about .12) in six out of seven lakes with nitrogen
to phosphorus loading ratios less than 10. This is evidence for possible
nitrogen limitation in a few of these impoundments and suggests that the
above model should not be employed under nitrogen-limited conditions.
Future development of this model might take into account the coupling
of nitrogen and phosphorus retention mechanisms.
Average outflow total nitrogen concentrations can be estimated from
the average inflow concentrations and estimated retention coefficients
according to the following:
Con = Cin(1-V (24)
where,
C = average outflow total nitrogen concentration (g/m )
With the retention parameter estimates listed above, Equation 24
explains 77 percent of the variance in log C , with a standard
10 on
error of .10. It is assumed that C is a reasonable indicator of
on
average epilimnetic total nitrogen concentrations, although no data
are available to substantiate this; the NES measured only inorganic
nitrogen concentrations within the impoundments.
Transparency
Transparency is an important water quality variable, not only for
aesthetic reasons but also because it influences the amount of light
244
-------
available for photosynthesis. Light penetration is considered to be
an important factor regulating the die-off rates of coliform bacteria
in natural aquatic systems (Chamberlin, 1978). Thus, increased transpar-
ency would also be expected to result in lower ambient levels of these
organisms. Pathogenic bacteria may be similarly affected.
The Secchi disc is commonly used to measure transparency in
impoundments. It can be approximately related to the light extinction
coefficient in the water column with model of the following form (Vol-
lenweider, 1974):
Z e = k (25)
where,
Z = Secchi disc transparency (m)
s
e = visible light extinction coefficient (m )
k = an empirical constant.
Holmes (1970) has suggested that a k value of 1.44 is appropriate for
turbid, coastal waters. Poole and Atkins (1929) suggested a value of
1.7. Simultaneous Z and e measurements performed by the Indiana State
s
Board of Health (1976) in eight impoundments have been analyzed to
verify the use of Equation 25 with k = 1.66, the geometric mean value
for the data set (Figure C-4). The possibility of a positive bias in
this relationship at high e values needs to be examined with additional
data.
245
-------
to
fc_
-------
The extinction coefficient, e, represents the fraction of visible
light energy absorbed per meter of depth, according to Beer's Law
(Wetzel, 1975) :
I
Y~ = exp(-eZ) (26)
•o
where,
I = visible light intensity at depth Z (cal/cm hr)
z *
2
I = visible light intensity at surface (cal/cm hr)
The light extinction coefficient can be approximately represented as a
linear function of four components (Lassiter, 1975):
e = e,, + e^ + <=« + e^ (27)
where,
e = extinction coefficient attributed to water (m )
w
e = extinction coefficient attributed to non-living, suspended
O
solids (m )
e_ = extinction coefficient attributed to algal biomass (m )
B
e = extinction coefficient attributed to dissolved color (m )
The first term, e , is on the order of .04 m , corresponding to the
W
maximum observed Secchi depth of about 40 m (Wetzel, 1975), and is
relatively insignificant in the impoundments being studied here. The
following linear relationships are used to estimate the remaining three
components:
247
-------
es = ksS (28)
eB = kBB (29)
ec = kcc (30)
where,
S = concentration of non-algal particulate material (g/m )
B = concentration of chlorophyll-a (g/m )
C = concentration of dissolved color (Pt-Cobalt Units)
k_, k_, k = empirical constants.
o B C
The calibration of three equations is discussed below.
Secchi depth and suspended solids measurements taken by the Illinois
State Water Survey (1977) in the Fox Chain of Lakes, Illinois, and by the
U.S. Army Corps of Engineers (1977) in five Indiana and Ohio impoundments
have been used to develop an estimate for k . Figure C-5 shows the relation-
S
ship between suspended solids concentration and the extinction coefficient
(determined from reported Z values and Equation 25) .. Average values were
O
reported for each of the Fox Chain of Lakes. Individual measurements
provided by the USAGE have permitted division of the data from each
impoundment into two, egual-sized groups, based upon solids concentra-
tions. The two summary points shown for each impoundment represent the
median e and S values in each group. The suspended solids concentrations
reported in these studies represent both algal and non-algal particulate
materials. It is assumed that the later dominate, since these data are
in the range from 2 to 80 g/m , while algal biomass levels would not be
248
-------
expected to be much in excess of 5 g/m , assuming a maximum chlorophyll-a
concentration of 100 mg/m . The lines drawn in Figure G-,5 correspond to a
o
k value of .085 m /g - suspended solids. Deviations of the data from
the lines are assumed to be attributed to variations in e + e , the
o Fox Chain of Lakes,
Illinois
• Ohio and Indiana
Reservoirs
i
10 20 30 40 50 60 70
S=Suspended Solids Concentration (g/m3)
Figure C-5. Relationship between Visible Light Extinction Coefficients
and Suspended Solids Concentrations in Corn Belt Impoundments
249
-------
water and color extinction coefficients. No independent color measure-
ments could be located to verify this assumption.
2
Further support for use of a k value of .085 m /g is obtained
s
from the results of Shannon and Brezonik (1972) who derived the
following relationship for Northcentral Florida lakes:
- = .003 C + .152 N (31)
z
s
where,
C = dissolved color (Pt-Co Units)
N = turbidity (JTU)
In terms of the extinction coefficient, equation (31) is equivalent to:
e = .005 C + .252 N (32)
With reference to Equation 27, the first term is attributed to dissolved
materials (e ), while the second is attributed to particulate materials
(e + e ). The average ratio of turbidity to suspended solids for the
b D
Fox Chain of Lakes is .32 JTU/(g/m ). Thus, in terms of turbidity, a
2 _]_
k value of .085 m /g is equivalent to .085/.32 = .266 m /JTU, which
S
agrees well with Shannon and Brezonik1s value of .252 m /JTU. Possible
variability in k attributed to different particle types and size dis-
O
tributions (Lassiter, 1975) suggests that the assumed value of .085 m /g
may only be appropriate for lakes in the region and not for rivers.
250
-------
Calibration or verification of the color term, e , cannot be
achieved directly because no color data have been located for these
impoundments. Shannon and Brezonik's results (Equation 32) suggest a
kC value of -005m /(Pt-Co unit). Color is assumed here to represent
humic acids derived from soil organic matter (Wetzel, 1975). The
method for predicting color loadings based upon computed runoff rates
and sediment organic matter content has been described previously.
Within an impoundment, color can be expected to decay as a result of
microbial degradation and adsorption/sedimentation processes. The
removal of color is represented here as a first-order reaction, in a
model similar to that employed for sedimentation:
c.
c = i£ (33)
oc 1+K T
c
where,
C = average outflow color concentration (Pt-Co units)
C. = average inflow color concentration (Pt-Co units)
1C
K = decay rate (year )
Secchi depth and suspended solids data from the upstream and downstream
ends of Mississinewa Reservoir (U.S. Army Corps of Engineers, 1977) have
been analyzed to develop an approximate estimate for K , the color decay
C
rate parameter. For each station and sampling date, a color concentration
251
-------
has been estimated by employing Equations 25, 27, 28, and 30 and the
parameter estimates derived above:
!i§§ - .085 S - .040
£"V"£w = ZS (34)
C k .005
Over a three-year period, the flow-weighted average inflow and outflow
color concentrations have been computed as 766 and 347 Pt-Co-units,
respectively. The mean hydraulic residence time over this period was
about .2 years. With reference to Equation 33, these values are equi-
valent to a K value of about 6 year
c
These data suggest that color is considerably more conservative
than suspended clay, the decay rate for which, according to Equation
2, is about 50 year . The apparent color decay rate is high, however,
compared with typical degradation rates of humus in soil systems, .01-.04
year (Buckman and Brady, 1966). This suggests that adsorption/
sedimentation may be the dominant color removal mechanism as discussed
by Otsuki and Wetzel (1974). More data are needed in order to further
calibrate and verify the relationships developed above for color degra-
dation and its contribution to the extinction coefficient.
The algal light extinction component e , is assumed to be propor-
tional to chlorophyll-a concentration, according to Equation 29. Riley's
(1956) data from mixed, natural, marine algal populations suggest that
252
-------
the proportionality constant k , varies somewhat with chlorophyll
B
concentration:
k = 8.8 + 5.4 B *33 (35)
B
2
According to this equation k decreases from 40 to 20 m /g as chloro-
B
phyll increases from .005 to .1 g/m . Other investigators (Lorenzen
and Mitchell (1973), DiTorro, et al, (1975)) have assumed constant
2
values of k within the above range. An average k value of 30 m /g
B B
is assumed here, although additional data and analysis could permit
better definition of the quantitative relationship between chlorophyll-a
concentration and light extinction.
The relationship between transparency and chlorophyll in the NES
impoundments is shown in Figure C-6. From Equations 25, 27, and 29, the
Secchi depth is given by:
k 1.66
Z_ =
S a+kB a+30B (36)
B
a =
(37)
Independent measures of the non-algal portion of the extinction coeffi-
cient, a, are not available for these impoundments. Accordingly,
Equation 36 has been plotted in Figure C-6 fijr various assumed values of
a ranging from 0 to 3. The locations of reservoirs on the plot relative
253
-------
• Natural Lakes
Reservoirs
.004 J005 006 .008 .01
.02 03 .04 .05 .06
B = Chlorophyll -a (g/m3)
.08 .10
.20
Figure C-6. Relationship between Secchi Depths and Chlorophyll-a Concentrations in
Corn Belt Impoundments
-------
to natural lakes indicate the relative importance of non-algal suspended
solids and color in controlling light penetration in the former systems.
To summarize, transparency is estimated according to the following
equation:
where,
V + kcc + V (38)
k = 1.66
ew = .04 m"1
2
k = .085 m /g suspended solids
O
k = .005 m V?t Co Unit
2
k = 30 m /g Chlorophyll-a
The three independent variables in this equation (S, C, and B) are esti-
mated for average summer conditions. Methods for estimating B are dis-
cussed in the next section.
Methods for estimating annual average S and C values have been
discussed previously (Equations 3 and 33). Summer concentrations of
suspended solids and color would tend to be considerably lower than
annual average values, due to lower input rates and longer hydraulic
residence times in impoundments during the summer months. Based upon
255
-------
analysis of data from Mississinewa Reservoir, Indiana (U.S. Army Corps
of Engineers, 1977), summer average color and non-algal suspended solids
concentrations are assumed to be one third of the respective annual,
flow-weighted-average outflow concentrations:
S = C /F (39)
OS CS
C = C /F (40)
OC CS
where,
F - 3.0
cs
A factor of two might be explained rationally by the fact that mean summer
flows are about one-half the annual average value in this region. This would
approximately double hydraulic residence times during the summer (unless im-
poundment is used for flood control) and thus provide twice as much time for
sedimentation and decay process. The additional reduction might be attributed
to lower inflow concentrations during the summer months. Additional data and/or
analyses are required to test and improve upon these assumptions.
Chlorophyll-a
Chlorophyll-a is a measure of phytoplankton densities in an impound-
ment. Along with hypolimnetic dissolved oxygen, transparency, and nutri-
ent concentrations, summer chlorophyll-a is often used as an indicator
of trophic state. In the interest of aesthetics, maintaining aerobic
conditions in the bottom waters of impoundments and ecosystem "health,"
256
-------
as indicated by the species present and their diversity, high chlorophyll
concentrations are considered deterrents to water quality. In the
interest of fish production, however, chlorophyll might be considered
beneficial in certain concentration ranges.
The method developed below for predicting chlorophyll levels in
corn belt impoundments is based largely upon theoretical considerations
and is empirically calibrated and tested using data supplied by the NES.
A basic assumption is that the growth of algal populations in these
impoundments may be limited by light, phosphorus, and/or nitrogen
supplies. The model is shown to have reasonable predictive capability,
despite the fact that other types of growth limitation (in particular,
carbon) have been ignored. Future improvements might be achieved by
considering the effects of such additional factors. The model is
developed below by (1) considering the limiting effects of each factor
separately; (2) subsequently combining these effects; (3) calibrating
empirically; and (4) presenting some evidence of verification. A
preliminary error analysis and an interpretation of the results are
also presented.
Light is a potentially important limiting factor, particularly in
the turbid and colored waters characteristic of impoundments in the
Oorn belt. The effects of light limitation on algal production are
represented below using a model originally developed by Lorenzen and
Mitchell (1973) and later modified by Sykes (1975) and Walker (1977).
The following simplified differential equation represents the growth
of algae in the mixed surface layer of an impoundment (Lorenzen and
257
-------
Mitchell, 1973).
g-= (y -
umax Js Ts
where
I = visible light intensity at depth Z and time of day t
z ,t
2
(cal/cm -hr)
2
I = saturation light intensity for algal specie (cal/cm -hr)
growth rate at optimal light intensity (days )
258
-------
Variation of light intensity with depth is represented by Beer's Law:
(43)
where,
2
I = surface light intensity at time of day t (cal/cm -hr)
o /t
e = extinction coefficient (m )
As noted previously, the extinction coefficient is a linear function of
algal density:
e = a + k B (44)
Variation of surface light intensity with time-of-day is represented by
a cosine curve (Vollenweider 1966):
I = .5 I (1 + Cos ) , - < t < (45)
o,t o,m A ^ 2
= 0 , otherwise
where,
2
I = surface light intensity at noon (cal/cm -hr)
o,m
X = day length (hours)
t = time from noon (hours)
By integrating Equation 45 over one daily cycle, it can be shown that:
259
-------
n,
,m
2 I
- ° (46)
where,
_ 2
I = total daily visible radiation (cal/cm - day)
With other nutrients present in excess, the steady-state, light-limited
algal density can be estimated by setting Equation 41 equal to zero, com-
bining with Equations 42 - 46, integrating over mixed depth Z and over
6
one, 24-hour cycle, and solving for B:
max
-~~ ' ~ (47)
[-PC-;
t =54 I |«P(-r^exp (-EZJ) -«p(-^i)|dt (48)
where,
B = light-limited biomass (g Chl-a/m )
L
F = Surface light depth-integral (dimensionless)
Z = Epilimnion depth (m)
e
For a totally absorbing surface layer (eZ :> 5),
the first term inside the integral of Equation 48 is essentially equal
260
-------
to one, and the integral can be evaluated numerically for the following
typical parameter values:
IQ = 240 cal/cm -day (McGauhey, 1968)
A =13.5 hours/day
2
1=2 cal/cm -hr (Parsons and Takahachi, 1973)
5
I and A values have been selected for an average summer day at 40°
latitude, assuming 75 percent of possible sunshine. The I value is at
5
the lower end of the range of experimentally determined values and is
thus appropriate for the shade-adapted algae which would be present
under light-limited conditions. Accordingly, the F integral has been
evaluated numerically to give:
F = .862 e — = 1.32 (49)
The value of this integral is rather insensitive to the assumed values
of I and I .
o s
Another factor which needs to be evaluated in Equation 47 is
U /&. Under light-limited conditions, the decay term, S, would be
governed by algal respiration, which is generally on the order of 10 per-
cent of the maximum photosynthetic rate (Parsons and Takahachi, 1973).
Accordingly, \i /& is assumed to be 10. The incremental light extinc-
tion coefficient due to algae, k_ has been estimated previously at 30
Dt
261
-------
m /g Chl-a. Substituting the above parameter estimates into Equation 47
gives the following result:
B -
-------
Zfch = thermocline depth (m)
Z = mean depth (m)
Snodgrass (1974) analyzed data from a number of northern lakes and
derived the following empirical relationship:
Z = 1.6 Z* (54)
Using Z, Z , and Z values derived from July temperature profiles
lUciX ufi
measured by ISBH (1976) in eight Indiana impoundments, Z values have
e
been calculated according to Equation 52 and compared with the predic-
tions of Equation 54. Agreement is reasonable, except for Z < 3 meters
in which Equation 54 gives Z values greater than Z. Accordingly, the
following empirical method is used to estimate Z :
O
Z = Z, Z < 3m
e —
Z = 1.6 Z'57, Z > 3m
(55)
This method is appropriate for early summer conditions and may be less
valid in reservoirs with unusual hydrodynamic characteristics.
Estimates of a, the residual, or non-algal component of the extinc-
tion coefficient can be derived from simultaneous Secchi depth and
chlorophyll concentration measurements according to the following
version of Equation 36.
1.66
- 30 B (56)
263
-------
When non-algal suspended solids and color measurements or estimates are
available, a, can be estimated independently of B according to the
following version of Equation 38.
a = .04 + .085 S + .005 C (57)
In the calibration work discussed subsequently, Equation 56 is employed
to derive a estimates from Z and B measurements in the NES impoundments.
S
When the model is used in a predictive mode, Equation 57 is employed to
permit estimation of a and B as a function of estimated suspended
L
solids and color concentrations.
Equation 50 indicates that a values greater than 13.2/Z will
prevent algal growth due to severe light limitation. Examination of
data from the NES has revealed one impoundment, Lake Springfield, with
a relatively low computed B value of .007 g Chi /m . The observed
L
mean chlorophyll-a concentration in this reservoir was .013 g Chl-a/m ,
almost twice the computed, maximum light-limited value. Similarly,
Lake Lou Yaeger (in the verification data set) has a computed B value
L
of -.060 g Chl-a/m and an observed concentration of .011 g Chl-a/m .
While errors in the data could be responsible for this, it is probable
that Equation 50 is not valid as B approaches zero. Light limitation
L
could not result in a complete absence of phytoplankton. Due to in-
complete horizontal mixing, shallow bays and littoral areas could support
algal growth in a turbid impoundment, despite the fact that average
conditions in the epilimnion might not. In calibrating and applying
the model, B is allowed to assume a minimum value of .020 g Chl-a/m .
L
264
-------
This assumption influences the computed B values of only two out of
L
the fifty impoundments used to calibrate the model.
The effects of phosphorus limitation upon algal production are esti-
mated based upon kinetic and stoichiometric considerations. Employing
Monod kinetics, the equation for algal growth as a function of available
phosphorus concentration under optimal light and other nutritional con-
ditions is given by:
ft • < i -« •
a p
where,
p = available phosphorus concentration (g P/m )
3.
K = half-saturation constant for phosphorus uptake (g P/m )
P
This equation is analogous to Equation 41 for light limitation and
assumes that light is available at optimal levels for algal growth
during the day. At the maximum, phosphorus-limited biomass level, the
available phosphorus concentration can be found by setting Equation 58
equal to zero and solving for P :
a
p = K / \ (59)
a pi, •"•" '
265
-------
Under these conditions it is assumed that the rest of the phosphorus
has been taken up by the algae:
Pt " Pa
-
where,
B = maximum, phosphorus-limited biomass (gChla/m )
y = algal p requirement (gP/m )
p = total phosphorus concentration (gP/m )
The following parameter values are assumed:
K = .01 g P/m3 (DiToro et al., 1975)
P
pmaX/<5 = 10 (Parsons and Takahachi, 1973)
y = 1 gP/gChl-a (DiToro, et al., 1975)
A =13.5 hours/day
Accordingly, Equation 60 can be evaluated as:
Bp = Pfc - .0022 (61)
Assuming that the median, summer total P concentrations reported by the
NES are representative at p values, B_ can be linked to average outflow
P concentrations using Equations 19 and 20 for natural lakes and
266
-------
reservoirs, respectively. These, in turn, can be related to average
inflow P concentrations and retention coefficients using Equations
17 and 18.
The effects of nitrogen limitation on algal production are repre-
sented in an analogous fashion:
n = K - (62)
a n , max
24 6
-,
BN = (nt - na)/YN (63)
where,
n = available nitrogen concentrations (g N/m )
Si
K = half-saturation consistent for nitrogen uptake (gN/m )
n = total nitrogen concentrations (gN/m )
Y = algal n requirements (gN/gChl-a)
B,, = maximum, nitrogen-limited biomass (g Chl-a/m )
N
The following parameter values are assumed:
K = .01 g/m3
y = 7 gN/gChl-a (Parsons and Takahachi, 1973)
Accordingly, B is given by:
N
267
-------
BN = (nfc - .0022)/7 (64)
This equation ignores the possible effects of nitrogen fixation by blue-
green algae and is therefore not valid under conditions in which that
phenomenon is important. It is assumed that n is related to average
outflow nitrogen concentration in a manner similar to that observed
in the case of phosphorus, although no data are available from the NES
to verify this.
Given the above expressions for the maximum light-, phosphorus,-
and nitrogen-limited biomass levels, a means of estimating the effects
of simultaneous limitation by more than one factor is required. A model
of the following general form is proposed for that purpose:
©••[<.• (y" •($•(#]
where,
B = observed, mean summer chlorophyll-a concentration (g/m )
m, f , f , f , f = empirical parameters.
One characteristic of the formulation is that, for m > 0, a relatively
low value of B /f would cause the corresponding term to dominate the
L L
right side of the equation. In that case, light would be controlling
the biomass level. Similarly, phosphorus or nitrogen could be con-
trolling. The parameter m determines the extent to which more than
one factor can be simultaneously important in determining the biomass
268
-------
level. As m increases, the relative magnitudes of the various limiting
factor terms become increasingly different, permitting only one term to
dominate at a time. As m approaches zero, the factor terms become
increasingly similar and the model approaches a multiplicative one. The
value of (f /B ) , for example, could be viewed as a measure of the
L L
resistance to algal growth attributed to light limitation. In that
sense, with f = 0, Equation 65 is equivalent to the formula for the
total resistance of an electrical circuit consisting of three resistors
connected in series. The empirical parameters have been included to
permit calibration of the model and testing of the significance of
each term.
Calibration of Equation 65 has been achieved by employing the
BMDP Nonlinear Regression Analysis Program, BMDP3 (Dixon, 1975).
Coefficients have been selected to minimize the sums of squares of
residuals, expressed as the differences between the observed and
estimated, transformed chlorophyll-a concentrations. The following
transformation has been found to give normally distributed, homoscedastic
residuals:
B = -1.//B (66)
where,
B = transformed chlorophyll-a concentration, (g Chl-a/m )
Optimal values of f , f , f , and f have been estimated for various
O L P N
assumed values of m, ranging from .125 to 2.5. In addition, K , a
269
-------
parameter in the light limited biomass expression (Equation 51), has
been optimized. Since the K value given in Equation 51 was derived
from a variety of theoretical assumptions and "literature" values of
the parameters y /6 and I , both of which are subject to error, optimi-
zation of this parameter is considered both desirable and permissable
without sacrificing the theoretical basis of the model.
Initial calibration runs using data from 50 impoundments have
indicated that optimal values f and f are not significantly different
from zero for any of the assumed values of m (.125, .25, .5, 1.0, 1.5,
and 2.0). With these parameters set equal to zero, the value of m
which gives the smallest mean squared residual is 1.0. Optimal coef-
ficients for this case are as follows:
f = 1.866 ± .149
P
f = 1.363 ± .333
Jj
1^ = .440 ± .052
With these coefficient values, Equation 65 explains 82.4 percent of the
variance of B , with a standard error of 1.378. Observations are plotted
against model predictions in Figure C-7.
Three strategies have been employed to test the model: (1) analy-
sis of residuals; (2) tests for parameter stability; and (3) tests on
an independent data set. Results of these tests are discussed below.
270
-------
to
-3X)-
-45
N -6.0
T
| -7.51-
o
5 -9.0
•o
? -12.0
in
8 -,,5
-16.5
-16.5
A A
AAA
A • A
A Reservoirs
• Natural Lakes
I
I
I
I
-ISO
-13.5 -12 X) -10.5 -9.0
Estimated Bt = - 1/VB", (
-7.5
-6.0
-4.5
Figure C-7. Relationship between Observed and Estimated. Transformed
Chlorophyll-a Concentrations in Corn Belt Impoundments
-3X)
-------
The residuals of the model have been tested for normality and
plotted against a variety of regional, morphometric, hydrologic, and
nutritional factors derived from the data in the attached tables
While formal statistical tests for normality have not been applied, a
normal probability plot appears to be linear (Figure C-8) . Examination
of other residuals plots has revealed a slight negative bias (averaging
about -.7 or one half of the standard error) in the ten impoundments
with hydraulic residence times less than .1 years. This may indicate
that flushing is an important removal mechanism (compared with respira-
tion, for example) in these impoundments. Future versions of the model
.«•+••••+• »»«*»•» • + •»«•*«• »••#•••••*•••«*•••» + •••«*••••*••»•*'•.••*•••§ + ••«•
2.4 ; ;
*
1.8
E
X_
P
E
C
. t_.
E
D
.I.Z
.60
N
O
f 0.0
M
V -.60
>
* *
**
*
**
**
***
**
**
* *
* *
L
U
E
=U2
*
*
-1.8
-2.4 *
- - . ».*t.t A «.«_»_.
-2.5 -1.5 -.50 .50 1.5 2.5 3.5
-3.0 -2.0 -1.0 0.0 1.0 2.0 3««>
• »».f «»
2.5
RESIDUAL
Figure C-8. Normal Probability Plot of Residuals from Chlorophyll-a Model
272
-------
could account for this by calculating 6 (Equations 41 and 58) as a
partial function of residence time. A plot of residuals against
longitude indicates a slight positive bias (again averaging about one
half of the standard error) in the seven impoundments east of the 83
meridian. The source of this bias is unknown. Aside from the apparent
biases discussed above (neither of which is statistically significant), no
systematic deviations have been detected in residuals plots.
Tests of parameter stability have also been performed in order to
develop some evidence of model verification. The data set has been
divided into two groups (23 natural lakes and 27 reservoirs) and
optimal f , f , and K values have been estimated for each group and
PL L
for assumed m values of.5, 1.0, and 1.5. An F test based upon residual
sums of squares (Dixon, 1975) has been used to test for significant
parameter variations across groups for each assumed value of m. Computed
F statistics for assumed m values of .5, 1.0, and 1.5 are 1.89, .93,
and 1.01, respectively, with 3 and 44 degrees of freedom. At the
90% confidence level, an F ratio of 2.43 or higher would indicate
significant parameter variations across groups. While this test is
only approximate in the case of a nonlinear model, the apparent stability
in the parameters is evidence for verification of the model and further
justification for the selection of an m value of 1.0, which resulted
in the lowest F ratio.
The model has also been tested using data from 20 other NES
impoundments in the Midwest, including seven from Illinois, one from
Indiana, three from Ohio, and nine from Iowa (listed in Attachment).
273
-------
Some of the data are from impoundments which were omitted from the
calibration data set for one or more of the reasons listed previously
(see Data Base). The computed standard error of B estimates for these
"t
20 lakes is 2.58, considerably larger than the standard error in the
data base used for calibration, 1.38. Examination of the residuals
reveals a strong negative bias (about three standard errors) in the
residuals from the three impoundments with overflow rates greater than
150 m/year or residence times less than three days (Charleston, Beach
City, and O'Shanghnessy). This suggests that flushing may be an
important algal removal mechanism in these impoundments, as noted in
the residuals plots discussed above. Another impoundment with a highly
negative residual, Lake Weematuk, was sampled only twice by the NES
during the summer of 1974. The chlorophyll estimate for this impound-
ment is therefore less reliable than for the others. Finally, outflow
phosphorus concentrations in Lake Aquabi were sampled only five times
by the NES, as compared with 12 or 13 samplings in the other NES
impoundments. If, for the above reasons, these five impoundments are
rejected from the data set, the standard error of the chlorophyll model
reduces to 1.38, in agreement with that observed in the data base used
for calibrating the model.
One potential problem with the parameter estimation procedure is
that estimates of the independent variable a, obtained from the NES
data according to Equation 56 are dependent upon observed Secchi disc
and chlorophyll values. Thus, in the above estimation procedure, B
appears implicitly on both sides of equation. It would have been
274
-------
preferable to have derived a estimates from independent suspended
solids and color measurements, had these data been available. This
2
procedure used for estimating a may have inflated the apparent R of
the model and the significance of the B term. The correlation
L
coefficient between a and B is .10, however, indicating that variations
in a are governed chiefly by variations in Secchi depth and are nearly
independent of B values. This suggests that ot is chiefly a measure of
non-algal turbidity and color and is not very sensitive to errors in
chlorophyll estimates. Variations in B , according to Equation 50, are
L>
also governed mostly by the changes in Z , as opposed to changes in a.
Thus, the problems arising from use of this procedure may not be impor-
tant, although the model should be verified using a estimates derived
independently, should such data be available in the future.
Using expected value theory, it can be shown that the coefficient
of variation of a chlorophyll-a estimate derived from this model is
given approximately by:
CVB = 2t 0 = 2.76 B (67)
where ,
B = estimated chlorophyll concentration (g/m )
CV = coefficient of variation of B
B
o = standard error of model =1.378
e
This equation does not consider the effects of parameter errors, which
would be important only at extreme values of the independent variables.
275
-------
At the average B value for the data set, the computed coefficient of
variation of B is .348. This corresponds roughly to a 95 percent con-
fidence range of ± 70 percent in the B estimate, a fairly wide error
margin.
A preliminary error analysis has been performed in order to parti-
tion the observed error into model and.measurement error components.
An important measurement error component is that associated with esti-
mating mean summer chlorophyll-a concentrations from grab samples taken
by the NES generally on three dates for each impoundment. This error
has been quantified by compiling and analyzing the spatially-averaged
chlorophyll data for each sampling data and impoundment. The computed
average coefficient of variation of the mean chlorophyll estimates for
fifty impoundments is .30. This can be compared with the model resid-
uals, which indicate an average coefficient of variation of .35, as
calculated above. Thus, an appreciable portion of the observed error
can be attributed to sampling errors in the mean chlorophyll values due
to temporal averaging. This does not include errors due to spatial
averaging. Other types of measurement errors are associated with the
independent variables in the model, including phosphorus concentrations,
Secchi depths and epilimnion depths. Any remaining error can be
attributed to the effects of factors not considered in the model. Based
upon the above analysis, that component is probably small compared with
the measurement error component. Thus, the actual model error is pre-
dicting chlorophyll values is probably considerably less than that com-
puted according to Equation 67.
276
-------
The insignificance of the nitrogen term in Equation 65 is not
surprising, in view of the excess nitrogen supplies in these impound-
ments, as discussed previously (see Nitrogen Trapping and Concentration).
The average value of B^ for the data set is .287 g Chl-a/m , compared
with average B and B values of .094 and .077 g Chl-a/m , respectively.
LJ P
Thus, nitrogen supplies for algal growth are about three and four times
in excess of light and phosphorus supplies, respectively. It is possible,
however, that inclusion of the nitrogen term in Equation 65 could b,e justified,
given data from impoundments with lower nitrogen concentrations. In
applying the model to assess soil management practices, the nitrogen
term is tentatively included with an assumed value of f equal to f
(1.866).
The empirically optimal value of K is .440 ± .052, identical to
L
the theoretically proposed value. This is surprising, in view of the
assumptions and literature parameter values which went into the
theoretical estimate. While other "theoretical" values of K are per-
L
haps equally justifiable, the agreement between the empirical and
a-priori values of this parameter lends some strength to the validity
of the model.
One theoretical interpretation of these results is that f /B and
L L
f /B are measures of the resistance to algal growth due to light and
phosphorus limitation, respectively. Figure G-9 plots these resistance
values, using different symbols for reservoirs and natural lakes. The
dashed lines in Fig. c-9 represent lines of equal biomass potential,
computed as the inverse of the sum of the two resistance terms, accord-
277
-------
400
o.Biomass . !
0 Potential " rL+rP
400
rP=fp/Bp = Phosphorus Resistance, (m/g Chl-a)
Figure C-9. Relationship between Light Resitance and Phosphorus
Resistance to Algal Growth in Corn Belt Impoundments
278
-------
ing to Equation 65. The potential ranges from about .003 g Chla/m in
the marl lakes of Northern Indiana to about .100 g Chl-a/m in Buckeye
Lake, Ohio. The solid, diagonal lines represent different ratios of
light resistance to phosphorus resistance. Most of the impoundments
fall below the main diagonal, where phosphorus is the dominant control-
ling factor. Light appears to be more important in reservoirs than in
natural lakes, as indicated by the relative positions of these two
groups on the plot. Higher turbidity, color, and phosphorus concen-
trations are typical of reservoirs in this data set. All of these
characteristics could be related to the lower geometric mean hydraulic
residence time of these reservoirs (.24 years), as compared with natural
lakes (.46 years). Due to increased trapping/decay of sediment, color,
and phosphorus, impoundments with higher residence times would be
expected to be increasingly phosphorus-limited.
The following equations summarize the predictive methodology
developed for mean summer, epilmnetic chlorophyll-a concentrations:
B = p - .0022 (68)
P t
BN - (nfc - .0022)/7
= -440 o_
L Z " 30
^ = 1.866 1.866 1.363 (71)
B BP BN BL
In applying this model to evaluate the effects of soil management
279
-------
practices on water quality, the following relationships are also employed
to estimate the independent variables:
Pt ' -778 Cop (72)
nt = .778 Con (73)
Z = 1.6 Z' , Z > 3m (74)
= Z , Z <_ 3m
a = .04 +..085S + .005 C (75)
The numerical constant in Equations 72 and 73 represents the
geometric mean ratio of median, summer total phosphorus to mean
annual outflow phosphorus in the fifty impoundments used for model
calibration. It should be noted again that inclusion of a nitrogen
term has not been empirically verified, possibly because of the exces-
sive nitrogen supplies in the impoundments used for calibration. Model
predictions under nitrogen-limited conditions are therefore considerably
less reliable than those made under phosphorus- and/or light- limited
conditions .
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pp. 53-83.
Vollenweider, R. A. "Calculation Models of Photosynthesis - Depth Curves and
Some Implications Regarding Day Rate Estimates in Primary Production
Measurements." Primary Productivity in Aquatic Environments, edited by
C. R. Goldman, University of California Press, 1966.
Vollenweider, R. A. "Input-output models with special reference to the
phosphorus loading concept in limnology. "Schweiz. Z. Hydrol., 37.
1975, pp. 53-84.
Vollenweider, R. A. Manual of Methods for Measuring Primary Productivity in
Aquatic Environments. IBP Handbook, No. 12, Blackwell Scientific Publi-
cations, Second Edition, 1974.
Vollenweider, R. A. "Moglichkeiten und Grenzen elementarer Modelle der
Stoffbilanz von Seen." Arch. Hydrobiol., 66, 1969, pp. 1-36.
Vollenweider, R. A. "Scientific fundamentals of the eutrophication of lakes
and flowing waters, with particular reference to nitrogen and phosphorus
as factors in eutrophication." Tech. Rep. DAS/C81/68, Organization of
Economic Cooperation and Development, Paris 1968, 182 pp.
Walker, W. W. Some Analytical Methods Applied to Lake Water Quality Problems.
Ph.D Thesis, Harvard University, University Microfilms, Ann Arbor, Michi-
gan, 1977.
Wetzel, R. G. Limnology. W. B. Saunders Co., Philadelphia, 1975.
283
-------
ATTACHMENT TO APPENDIX C
Tables of Data Used in Calibrating
and Testing Impoundment Models
Key to Symbols Used in Data Tables
ID= u.S.E.P.A. National Eutrophication Survey Working Paper Number
NAME= Impoundment Name
STATE= Location
TFDPHIC= Trophic State (EUTR= Eutrophic, MESO= Mesotrophic, OLIG= Oligotrophic)
TYPE= Impoundment Type (RES= Reservoir, NAT= Natural Lake)
LATI= Degrees, North Latitude
LONG= Degrees, West Longitude
2
AS= Surface Area (km )
2
AD= Drainage Area, excluding impoundment surface, (km )
Z= Mean Depth (m)
ZMAX= Maximum Depth (m)
T= Mean Hydraulic Residence Time (years)
QS= Surface Overflow Rate (m/yr)
2
LP= Total Phosphorus Loading (g/m -yr)
RP= Total Phosphorus Retention Coefficient (dimensionless)
CIP= Average Inflow Phosphorus Concentration (g/m )
COP= Average Outflow Phosphorus Concentration (g/m )
UP= Phosphorus Settling Velocity (m/yr)
LN= Total Nitrogen Loading (g/m2-yr)
284
-------
RN= Total Nitrogen Retention Coefficient (dimensionless)
CIN= Average Inflow Nitrogen Concentration (g/m )
CON= Average Outflow Nitrogen Concentration (g/m )
UN= Nitrogen Settling Velocity (m/yr)
CHLA= Mean Summer Chlorophyll-a Concentration (mg/m )
ALPHA= Non-algal Portion of Visible Light Extinction Coefficient=
- -°3CHLA (nrl)
ZSEC= Mean Summer Secchi Depth (m)
DOMN= Minimum Hypolimnetic Dissolved Oxygen Concentration (g/m )
TPM= Median Summer Total Phosphorus (g/m )
OPM= Median Summer Ortho- Phosphorus (g/m )
INM= Median Summer Inorganic Nitrogen (g/m )
2
ST= Sedimentation Rate (kg/m -yr)
LS= Apparent Sediment Loading (kg/m2-yr)
2
LP' = "Corrected" Total Phosphorus Loading (g/m -yr)
RP'= "Corrected" Total Phosphorus Retention Coefficient (dimensionless)
UP' = "Corrected" Total Phosphorus Settling Velocity (m/yr)
285
-------
Table C-A. Data Used for Model Calibration
(O
00
ID
296
297
301
309
312
313
315
NAME
BLOOM 1NGTC1N
CARL VLE
CRAD ORCHARD
LONG
RACOON
RENO
SHELBVVILLE
317 5PRINGFIELO
318 STOREY
320 VERMILION
322 WONOER
323
324
325
H»-
328
330
332
333
334
337
338
340
342
344
346
347
3*8
349_
393
395
306
398
399
400
AOl_
402
403
406
4 OB
409
411
OASS
CATARACT
CROOKED
DALLAS v
GfclSt
HAMILTON
JAMES LAKE
LONG
MARSH
Ml SSI SSI ME WA
MORSE
QLIN
P I GEON
TIPPECANOE
WA WA SEE
WE BS T ER
W6STLER
WHITEWATER
W I NONA
WITMER
ATWOOO
OERL IN
BUCKEYE
CHARLES MILL
DEEPCREEK
DELAWARE
DILLON
HOLIDAY
HOOVER
MOSQUITO CR.e
PLEASANT HIL
ROCKFQRK
ST MARYS
MPAN
STD OEV
MINIMUM
MA X I CUM
STATE TROPHIC
ILL
ILL
ILL
ILL
ILL
ILL
ILL
ILL
ILL
ILL
ILL
INO
INO
INO
INO
INO
INO
INO
INO
INO
I MO
INO
INO
IND
INO
INO
INO
INO
INO
IND
CHIO
OHIO
OHIO
OHIO
fHIO
OHIO
OHIO
PHICL
CHIO
OHIO
CHIP
OHIO
OHIO
EUTR
EUTR
EUTP
EUTR
EUTR
EUTP
EUTR
EUTR
EUTR
EUTP
fUTR
EUTP
EUTR
ME SO
EUTR
EUTH
EUTR
EUTR
MESO
EUTP
EUTR
EUTR
ME,SO
EUTR.
EUTR
MESO
•IE SO
MESO
EUTR
EUTR
EUTR
EUTR
EUTR
EUTR
EUTR
EMI"
EUTR
EUTR
EUTP
EUTP
EUTP
FUTR
EUTR
EUTP
EUTR
TYPE
PFS
RES
R.ES
NAT
NAT
RES
RES
RES
RES
RES
RES
__8ES._
NAT
RES
NAT
NAT
«?es
NAT
NAT
J^AT
NAT
NAT
RES
NAT
RFS
RES
NAT
NAT
NAT
NAT
NAT
NAT
NAT
RES
NAT
NAT
RES
R£S
NAT
"*ES
RtS
RES
RES
NAT
RCS
RES
Res
RES
RES
NAT
LATI
40.650
30.670
37.720
32.800.
42.380
38 .550
3B.080
_3.9,5QO_
39.720
40. =30
40. 170
Al"!~220
39.480
41 .670
_4.J«.S5fl_
39.920
41.550
41.320
41_.3QQ_
41.980
41 .720
40.670
LONG
33.920
89.250
89.O80
_fl8i08Q_
88.1 30
89.080
89.97O
_BB.$3.0_
89.600
90.400
37.650
86.580
86.920
35.050
85. 420
85.950
84.920
85.730
_B5.Q30_
45.030
84.980
95.920
39.080 86.420
40.080 86.030
41.570 85.390
_*..!.. SflO 35..4QCL
41.640 34.950
41.330 85.770
41.400 85.700
41.320 85.670
41 .320
39.610
41.220
41 .530
40.540
41.000
39.920
— 4..Q.t_7.5.Q
39.720
4O.330
40.000
35.390
84.970
85.830
_.B5,Ai>0_
81 .250
81.080
82.500
83.250
33.170
82.0RO
33.930
41.100 82.730
4O.080 82.870
41.330 80.750
__» 4. fiJO B2.. 12 0_
39.180 83.500
40.530 84.500
40.558 85. 566
1.094 2.431
37.720 80.750
42.380 90.4OO
40.710 8S.410
AS
1 .970
105.200
28. 190
._l_J.i350_
1 . O3O
3.010
76.490
1 7 . 1 30
0.530
2.830
2.95O
AO
178.0
6937.0
492.4
24ia.O
98.7
122.0
1224 .O
_£665.0_
664.1
17.7
771.6
249. O
5.690 7.7
5.660 7*6.3
3.250 27.6
L.LSQ UU«JQ_
7.290 S52.0
3.250 39.6
1.140 144.8
4_.leO 1 19.6
0.370 175.7
0.230 3S.4
12.750 2070.0
7.54O 28. O
43.500
5.570
0.420
1 .500
O.250
3.110
12.380
2. 37O
0.360
0.810
2.270
0. B3O
6.230
8. 900
12.710
5.46O
5. 170
S.260
S. 360
.500
6.700 23.500
2.1OO 13.7OO
6.100 11.600
4.600 14.900
9.100 24.400
_J_0.4DQ L6.50Q
4.700 8.200
4.900 14.000
1.900 4.000
1.700 9.400
5.000 10.400
3.}OO 9.40O
3.000 7.600
1.900 8.100
3.900 4.900
6.500 I7.6OO
2.7OO 6.100
5.100 12.100
3.000 7.500
5. OP* 13-942
2.323 8.510
1.200 3.700
12.200 37.500
....4.750 ..11 .300
T
0. 200
o. teo
0.79O
__0.030
0 .OPS
0.200
1 .250
_Qj36Q
0. 480
0.770
0.025
0. 150.
3.200
0.140
2.600
Q.9 10
0. 160
1 .800
0.220
0.036
0.120
0. 140
__fi.70Q
0.660
0. 150
1 . 100
2»30Q
0.047
0.410
3. 500
0.130.
0.074
0.260
0.8"»0
Q. 320.
0.49O
0.222
0. 640
0.120
0.063
0.025
0.280
0.490
0.960
_fl. 09.fr
0.390
1 .600
C.726
1. 170
0.025
6.700
*s
17. 241
15.000
3.797
4S.6*7
16.162
6.000
3.7fo
13.880
8.333
5.974
56.000
16.667
0.561
43.571
2.346
26.34,1
22. 50O
3.500
37.273
p,. 4 an
141 .£67
50.833
51 .429
8.030
31.333
10 .636
97. 872
27.561
I .014
16* 154
82.432
17.692
10.460
32.500
9.592
22.072
2.969
30.009
41 .667
52.3B1
120.000
13.929
13.265
?.812
50.000
13.077
1.B75
96 .918
30.237
0.563
141 .667
18.410
-------
Table C-A (cont'd). Data Used for Model Calibration
ID NAME
LP
RP
CIP
COP
UP
LN
RN
CIN
CON
CD
-J
. 8.96 0-00M INGIflN
297 CARLVLE
301 CRAB ORCHARD
302 OECATUR
... 309 LONQ
312 RACOON
313 RENO
315 SHELBYVILLE
—3 1 7_SPR INGF1EL D
318 STOREY
320 VERMILION
322 WONDER
.... 323.B4SS
324 CATARACT
325 CROOKED
326 DALLAS
327 GE 1ST
328 HAMILTON
330 JAMES LAKE
331 L JAMES
332 LONG
2J3 MARSH
334 MISSISSINEWA
335 MA-XINKUCKEE
336 MQNROE
317 MOHSE
338 OLIN
339 OLIVER
3 » Q ,£\ GE DN
342 TIPPECANOE
344 WAWASEE
345 WEBSTER
346 WESTLER
147 WHITEWATER
34« MI NONA
349 WITHER
3?3 ATWCOO
395 BERLIN
396 BUCKEYE
397 CHARLES MILL
398 OEERCRBEK
399 DELAWARE
400 DILLON
401 GRAMT
402^ HO'.IOAt
403 HOOVER
406 MOSQUITO CR£
40B PLEASANT HIL
409 RPCKFOHK
411 ST MARYS
MEAN
STp pFV
MI NIMUM
MAXIMUM
MEDIAN
2 . 1 70 c . 3J 0 JO..J 26
3.0OO 0.390 0.2OO
2.820 0.780 0.743
9.150 0.260 0.196
_2 3 -.6.6 Q _Q .6 20. i.. 3QJ
1.170 0.280 0.195
0.970 0.500 0.258
4.120 0.660 0.297
_. 1 • ?.0g__0 .250 0, 204
2. IRQ 0.690 0.365
10.200 0.330 0.182
12.390 0.560 0.743
._ 0.090 0..44.0 O.I 60
5.650 0.430 0.130
0.240 0.790 0.102
1.530 0.070 0.058
2.880 C.400 0.128
0.320
1.300
0.340
29.540^
0.470
-O. 030
0.590
0.330
6.090 0.060
12.590 0.490
0.150 0.670
Ot2flQ_ 0.430
6.520
0.810
O. 160
8.32.Q_
1.1 OO
0.110
1 .040
4.390
3.200
0.800
2.730
1 .670
5.870
0.510
5.560
5.540
12.490
32.910
8.510
8.410
0.530
0.790
0.560
D.20Q_
O.290
0.640
0.500
B.I 10
0.640
0.400
C.330
Q.6.5Q_
0.760
0.1OO
0.170
0.200
0.420
C.510
0.150
0.800
1.740 0.650
0.320 0.440
3.510 0.210
2.050 0.710
0.490
5.067
Z.-Q46^_
0.090
32.910
2.455
0.310
0.442
0.225
-0.030
0.870
0.440
O.091
0.035
0.040
0.209
0.087 7.746 174. ICO -O. 1 OO
0.122 9.590 4G.2OO 0.10O
0.163 13.464 13.000 O.550
0.145 16.396 239.600 -O.19O
_tt • 4.94 29 . 665_1 1.4 . 8 Q 0 0 .S5O_
0.140 2.333 19.500 O.430
0.129 3.760 9.900 0.420
0.101 26.961 a£. 900 0.220
_fl*J5.3 2«_77fi C2..9JU1 Q.J40_
0.113 13.297 43.500 O.280
0.122 27.582 388.000 0.070
0.327 21.212 82.900 0.410
_fl .090 0 .442 3 . 300 Q. 420_
0.074 32.870 1 83 . 1 OO 0.260
0.021 8.826 6.500 0. 54O
0.054 1.983 62.90O 0.330
O.O77 1S.OOO 91.4OO -O.O2O
0.048 3.104 12.00O 0.530
0.036 -1.086 8P.ROO 0.040
0.016 12.215 13.400 0.460
0.140 69.776 5
-------
Table C-A (cont'd) . Data Used for Model Calibration
10 NAME
CHLA
ZSFC
ALPH
OOMN
TPM
0PM
I MM
10
00
00
»i>65 8A?J»Q_
0.050
301 CRAB ORCHARD
102 DECATUR
309 LONG
312 RACOON
313 REND
315 SHELBYVILL6
19.200
17.200
Ifr&l-
31.200
9O.500
11 •,
0.399
0.724
0.983
2.422
1 .875
1.914
las
3 .587
i.sae
1.173
5.730
1 .27C
4.000 O.OQ4 O.032 1.270
2.000 0.082 0.013 0.2OO
0.500 0.129 0.002 3.7SO
.200 0.704 0.398 1 . 1 9O
1.200 O.I 06 C.020 0.310
2.300 0.071 0.012 0.21O
1.000 0.062 0.019 3.290
4.200 O . 1 08 O . 059 3. g7O
324
325
326
127
CATARACT
CROOKED
DALLAS
ST
MARSH
MISSISSINEWA
HAXIKKUCKEE
MONROE
ORSK
_
395 OERLIN
396 BUCKEYE
397 CHARLES MILL
.398.
399 DELAWARE
400 DILLON
401 GRANT
402_HQL.IO*.r
403 HOOVCR
406 MOSQUITO
408 PLEASANT MIL
4 09 ROCKfORK
4"! 1 ST MARTS
»* AN
MI NI MUM
MA XI MUM
MEDIAN
0.470
0.356
OQ _ O.JT26
1O. 700 O.ft46
5.5SO £.203
10. 10O 2.202
go
f.SOO
11.500
4.660
_9«.Lflfi 9.,.Q5A.
0.072 0.021
1.642
O.S60
0.451
-*m—-fc
1.676
3.749
0.200
2.597 0.600 0.109 0.05O
1.953 7.200 0.426 0.132
• » 03 8_,.Q40 Q ,Q4Q fl.,
348 MI NONA
349 Wl TMFR
34.500
15.800
5.400
6.^95,0
56.200
4.S70
3.770
J1.2Q9.
6.050
5.000
II.500
P. Z go I.JBSZ
_1
33.100
II.200
11.900
6t»0fl
15.500
106.600
67.1OO
-..
1.237
0.676
2.530
8 _ L.-554
0 0.
0.011
0.005
0.014
SUQ.09_
0. 01B
0.006
0.005
I. I 50
0.055
O. 029
0.757
1 .405
1.516
0.965.
O.B/9
0.254
0.4*5
9.899. 0«J7_59_
10.a4O 0.404
Z7.400 0.475
4O.5OO 0.3*8
55*400 0.691.
I3.OOO 0.945
36.300 0.891
22.900 1.O97
-2e.iQ.QQ. : :
79.200 0.401
.at.?**..
0.421
0.332
0.602
0.575.
1 .200
0.646
0.738
j.eza.
1 .424
0.937
1 .722
_l«Jlfl9-
3.785
2.673
3.555
.O...Z2L.
1 .367
O.794
0.026
-U28^
I .760
0.400
0.0
_L*0_
0.0
0 .0
0.0
.0.300 O.O3J-
1.400 0.042
5.360 0.179
0
0.019
0.012
0.025
£«o.as-
O. O84
0.03S
0.035
O. 009
0. OO3
0.004
-0«J>JL5_
0.005
0.003
C.OOS
_0-i.913_
O. 012
C.Ot 1
0.011
0. 005
0.006
0.020
0 0.127 C.011
IQQ Q . 048 0 » 0 3«
0.600
0.700
2. BOO
_O«0
O.?00
3.400
O. 500
^6* 8OO
086
163
I 13
2.510
4.7QO
0.090
1.660
0.120
O.R30
JjQBQ
0.720
1.030
0.190
1.920
0.270
2.400
0.003 0.220
fi • i> 25 O. 007 O.?30
3.330
1.460
0.920
_L.i50_
0.200
0.210
0. 700
1^620
1.250
0.000
_0.24S
0.000
0.380
0.465
28.MB 1.187 1.327 1.295
..30,373 0.949 Q.937 2115
9.770 0.254 0.221 0.0
186.6OO 3.7*9 3.705 Q.OOO
17.350 0.861 1.076 0.200
O.04O
0.058
O.036
i067_
0.148
0.008
_0.«.L12-
0.009
0.7O4
0.060
0.024 2.340
0.037 1.500
0.010 0.57O
.a3» 0.^15
0.008 I.64O
0.006 0.150
0.010 0.4*i5
Q».QJQ 8.700
O.014 0.200
0.002
0.398
0.01Z
301
2fi&
0. 120
5.730
0.895
-------
Table C-B. Sedimimentation Data Used for Phosphorus Retention Model Calibration
CD
296
297
301
302
312
317
320
347
395
396
397
399
401
408
411
ID NAME
Bloomington
Carlyle
Crab Orchard
Decatur
Racoon
Springfield
Vermilion
Whitewater
Berlin
Buckeye
Charles Mill
Delaware
Grant
Pleasant Hil
St Marys
Mean
Std Dev
Minimum
Maximum
ST
12.860
14.500
10.180
19.270
4.444
7.180
23.300
32.570
71.030
3.020
12.390
13.610
13.190
16.520
6.000
17.338
16.675
3.020
71.030
LS
13.512
15.685
10.369
28.716
4.771
7.400
37.006
34.412
76.735
3.089
15.703
16.787
16.085
19.051
6.055
20.292
18.415
3.089
75.735
LP
2
3
2
9
1
1
10
3
5
0
5
12
8
3
0
4
3
0
.170
.000
.820
.150
.170
.700
.200
.280
.870
.510
.560
.490
.510
.510
.490
.695
.783
.490
12.490
LP'
3
4
3
11
1
2
13
6
11
0
6
13
9
5
0
6
4
0
13
.251
.255
.650
.447
.552
.292
.160
.033
.929
.757
.816
.833
.797
.034
.974
.319
.585
.757
.833
RP
0.310
0.390
0.780
0.260
0.280
0.250
0.330
0.640
0.760
0.100
0.170
0.420
0.150
0.210
0.310
0.357
0.211
0.100
0.780
RP'
0.539
0.570
0.830
0.409
0.457
0.444
0.481
0.804
0.882
0.394
0.323
0.746
0.262
0.449
0.653
0.531
0.185
0.262
0.882
UP
7.746
9.590
13.464
16.396
2.333
2.778
27.582
31.453
69.895
0.330
6.331
37.931
5.004
13.291
0.842
16.331
18.768
0.330
69.895
UP1
20.193
19.875
18.541
32.230
5.052
6.647
51.841
72.702
164.820
1.928
14.745
47.642
10.049
40.772
3.529
34.038
41.646
1.928
164.820
-------
Table C-C. Data Used for Model Testing
IO NAME
STATE TROPHIC
TYPE
LATI
LONG
AO
7MAX
T,
QS
295 BALDWIN
299 CHARLESTON
308 HORSEHOE
_ 3 14>_L°_U Y-*.C GCR
314 SANGCHRIS
316 SLOCUM
321 WEEMATUK
341 J51LVAN
" 304 BEACH ClTY
407 OSHAUGHNF.SSY
410 SHAW NfE
_494 AHQUABJ
495 BIG CP.RFK
496 BLACKHAWK
500 MACBOIOF.
_5 Ol_ P» ABI F_ROSE
503 PEO ROCK
504 ROCK CPEEK
so7 viKING
_5_0 5_ SI L.V EB
EUTR NAT
_«y»T.E CES_
3B.220 89.87O 8.00O
39.470 89.150 1.450
38.700 90.080 e.78O
39, ZOO &91.6QQ 5*-7.29_
39.630 89.470 10.930
42.260 BO.190
40.530 90.150
4.L 1.48.0. 85,3.70.
40.630 81 .500
4O.16O 83.12O
39.650 83.780
41 ..260 93.590 0.530
41.600 93.75O 3.440
42.30O 95.040
41.610 91.550
_RES 4J .-, MO 95.,
PCS 41.430 93.07O
RFS 41.150 92.850
RES 40.980 95.030
RES
NAT
RFS
3 . 7 2O
3.840
EUIR._ NAT 43.4BQ 23*420-
_.
36.220
2.600
0.610
MF AN
STD OEV
MA XI MUM
MEDIAN
40.617 90.139 4.981 1920.4
1.356 4.123 7.91O 7O8S.6
_3 6*730 9L..5M O^SJQ 4.6
43.48O 95.200 36.220 31680.0
41.130 90.115 ?.S75 70.2
7.9OO
0.003
1. 150
Jl.330.
1 .200
0.330
0.450
3.100 12.800
O.900 1.500
2.100 -1.000
i.30_Q 6_,ZOO_
.000 IO.OOO
.200 1.500
.000 6.100
_*30Q IJ. ..0.0ft OA-4.4.0
?00 3.000 0.008
800 15.500 O.O2S
2.500 7.600 0.200
ia.000 ft. 600 _O.I20._
6.700 15.500 0.740
1.700 3.700 0.^40
7.300 14.200 2.200
_3j3QQ S-> »-0
3.000 10.700 0.027
2.7OO 6.700 0.390
5.800 12.5OO 1.600
_i_*24Q x.aoo.
146.341
I92.0OO
12.500
3.195 7.695
1.932 4.941
0.9QO -1.QQQ
7.300
3.000
15.500
7.150
1.043 41.402
1.719 81.732
Q.OO1 0.39?
7.900 300.000
0.585 4.OBJ
10
V0
O
Table C-C (cont'd). Data Used for Model Testing
10 NAME
LP
RP
CIP
COP
UP
LN
CIN
CON
UN
295__
299
308
310
31.4.
116
331
341
407
410
494
4 95
496
500
501
-5Q3-
504
507
505
BALDWIN
CHAPLFSTON
HORS6HOE
LOU YAFGER
SANGCHRI S
SLOCUM
WEEMATUK
SYLVAN
BG.AC.H Cl TY
OSMAUGHNeSSY
SHAW NEE
AHQUARI
Bl G CRfEK
0.580 0.980
52.510
0.510
3.150
0.3SO
11 .180
0.610
1 .260
27, I5.fi
70.730
0.600
1.020
?.?TO
BLACKHAWK 0.550
MACRRIOE 0.67O
PRASIF. ROSE 0.720
REJT>_fig_CE_ 6a.96Q_
ROCK CR5FK 3.120
VIKING 0.490
SILVER 0.240
MEAN
STO OEV
MI NIMUM
MA XI VUM
MEDIAN
12.335
23.357
0.240
70.73.0
0.070
-0.030
0.160
O.I 1 0
0.400__
0.600
0.530
-0.060
O.~350~
-0.200
0.670
0.74O
0.200
0.720
0.7*0
C.610._
0.820
0.430
0.360
0.416
0.325
-0.200
0*415""
1.478.
0.175
0.279
0.315
_O.Ji4
3.O75
0.152
0.129
_0*1J86_
0.368
0 .048
0.245
_0.251_
0.239
O.202
0.262
O m 62. 1
0.451
0.135
0.240
0.448
0.688
0.048
3 ,075
0.242
0_iQ3.Q
0. 180
0.235
0. ?80
7. 100
-8. 73823B3. OOO
0.348 3.500
1.236 46.600
9.999 ?3.IOO
1.210 5.455
0.072 4.511
0.137 -0.553
_0_tJ54 29,*9. 74_
0.239 103.385
0.058 -2.063
0.481 8.460
0.465 25. I69_
0.192 0.574
0.057 8.532
0.068 7.827
Q t 242 1 73 «X&9_
0.001 31.538
0.077 2.735
0.149 0.613
31 .800
24.600
44.600
506. 7 OO
986. 700
49.500
21 .400
_LD7.300_
21 .600
16.900
16.000
_a3a.*aa0_
35.500
7.200
3. OOO
0.185 20.741 258.349
0.257 43.265 575.196
O.C30 -8.738 3.500
1.230 173.7B923a3.JLQQ_
0. 109
4.983
28.200
..0.930
0.170
-1.670
0.35O
0.44A
0.33O
0.350
0.410
Q»J2CL-
0.0
0.200
0.62O
o..*.5a_
0.700
O.S6O
0. 67O
0. ?6O
0.2*O
0. *6O
-0. IOO
0.272
0.519
-1.670
0.930
0.35O
_LB.J)9»
7.943
1.917
4.660
P. 745
6.150
4.564
3.462_
5.1 39
3.960
5.136
_jj..asi
0.402
5.093
s.eia
7.47O
5.128
1.986
3.900
6.367
7.688
1.917
1K.094
5.13Q
_1..267 5..2J3
6.593 61 .446
5.117 -1.142
3.029 5.385
3.534 3.203
5.859
3.998
2.f 9T
__3.0*7_
5 .1 SO
3.168
1 .952
6.51B
1.791
2.154
6.^91
19.956
0.0
3.125
6.79B
7.40B
2.B21 5.360
2.241 4.223
1.920 5.583
5. S2* 30.010
3.897
4*290
3.694
1 .655
1 .267
6.593
3.351
2.186
2.039
-O.O91
9.023
15. 198
-1 .143
61. 4*6
4.718
-------
Table C-C CCont'd}. Data Used for Model Testing
to
VO
ID NAME
295 BALDWIN
308 HORSEHOE
310 LOU YAEC.ER
314 SANGCHR1S
316 SLOCUM
321 WEEMATUK
341 SYLVAN
394 BEACH CITY
4"b7 Q5H*UGHNFS~S\
410 SHAWNEE
494 AHQUAB1
_495_niG CREEK
406 BLACKHAWK
500 MACBRIOE
501 PRA.RIE POSE
503 f>ED ROCK
504 WtfCK CftEFK
507 VIKING
505 SILVER
Mt AN
STO oev
MINIMUM
MA XI *>UW
MEDIAN
CHLA
U. 300.
.000
1H2.300
10.700
19.300
221 .100
a. ooo
47.5OO
10.870
5.520
39.600
8.60O
16.900
49.700
17.100
17.400
J4.70Q
18.400
26.0OO
95.300
41.614
58.945
5.520
221 .100
17.250
ZSEC ALPH DONN
0.986 J.345 1.800
0.236- 6.667 6 . 60O
0.437 0.0 B.200
0.264 5.963 3.600
Q.625. 2f078 g. 5.0Q.
O.323 O.O 9.200
O.BS6 1.699 O.SOO
0.767 0.739 O.ZOO
0.279 5.tl5 4.000
0.526 2.992 0.100
0.653 1.355 0.0
O.TflO 1.671 6. BOO
I.5&2 P. 556 0.2QQ
0.300 4.047 0.0
1.057 1.058 0.0
0.922 1.276 6.400
A »_6 76 __£.».P. 16 L.flQO_
0.49S 2.799 6.600
1.041 O.aiA 0.600
0.439 0.919 5.000
0.661 2.191 3.075
0.342 1.950 3.216
0.236 0.0 0.0
1.562 6.667 9.2OO
0.639 1.527 1.400
TPM OPM
0.044 0.007
0.160 0.065
0.127 0.018
O.lfl6 O.O76
o.psp p. oq?
O.B05 0.302
0.069 0.011
0.1 70 0. Ol 7
Q.I 22 O.pl5
0.208 0.159
0.060 C.009
0.062 O.O09
P..P»5 JB. Oil
0.185 0.020
0.061 0.010
0.056 0.010
~~olo65 otoo7
0.075 O.O17
0.193 0.034
0.147 0.046
0.166 0.072
0.044 0.007
0.805 0.302
O.O9R 0.017
INM
4.600
0.705
1 .600
1.970
0.20O
1.770
0. 1 30
1.99P_
3.070
2. 380
0. 335
6.470
0. 1 30
2.040
0.210
O."l30
O.S70
1.566
1.656
0.130
6.47O
1 .500
-------
Appendix D
Water Quality Impact Results:
Additional Interpretations and Sensitivity Analysis
Introduction
In Section 5 of this report, the application of the watershed and
impoundment water quality models is discussed. The purposes of
this appendix are (1) to present the details of the water quality impact
results; (2) to present some supplementary interpretations of these
results; and (3) to present some preliminary results of a sensitivity
analysis applied to the watershed/water body model framework .
Water Quality Impact Results
The watershed and impoundment models have been applied to assess the
water quality impacts of each of 11 agricultural practices on each of
three field/soil types.-.characteristic of the. Black Creek Watershed,
Indiana. For each practice/field/soil type combination, the analytical
framework has been applied to a homogeneous watershed of 200 km draining
2
into an impoundment with a surface area of 5 km and a mean depth of 4
meters. Table JD-1 identifies some of the key variables used to depict
the water quality impact results. These results are presented in Tables
D-2, D-3 and D-4 for the lowland, ridge, and upland soil types, respectively.
292
-------
TABLE D-l. DEFINITIONS OF VARIABLES IN TABLE D-2 to D-4
Number
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
Definition
Runoff rate
Gross erosion
Sediment delivered to impoundment
Sediment trapped in impoundment
River nitrogen concentration
River phosphorus concentration
River sediment concentration
River light extinction coefficient
Soluble phosphorus loading
Snowmelt (crop residue) phosphorus loading
Available particulate phosphorus loading
Total phosphorus loading
Impoundment outflow nitrogen concentration
Impoundment outflow phosphorus concentration
Impoundment outflow sediment concentration
Impoundment light extinction due to sediment
b
Impoundment light extinction due to color
b
Impoundment light extinction due to algae
b
Total impoundment light extinction
b
Secchi disc transparency
Annual average impoundment light extinction
coefficient
b
Nitrogen resistance to algal growth
Phosphorus resistance to algal growth
b
Light resistance to algal growth
Total resistance to algal growth
Chlorophyll-a concentration
Units
m/yr
kg/m2-yr
A
kg/m -yr
kg/m2-yr
g/m3
g/m3
kg/m3
m
g/m2-yr
g/m2-yr
g/m2-yr
g/m2-yr
g/m3
g/m3
kg/m3
b
m
m 1
_
m
-l
m
m
m"1
(g-Chl-a/m3)"1
(g-Chl-a/m3)"1
(g-Chl-a/m3)"1
(g-Chl-a/m3)"1
g-Chl-a/m3
b - Summer average values.
293
-------
TABLE D-2. WATER QUALITY RESPONSE TO PRACTICES FOR LOWLAND SOIL
PRACTICE:
10
VO
ICE:
able
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
1
. icc-cv
0.173
O.762
4.957
4.763
1 1 .076
O.I 86
0.496
47.477
1 .105
0 .O
0.750
1 .656
5.443
O .093
O.C19
0 .551
0.520
0.663
1.773
O.936
3.252
3.CE6
26.661
15.532
45.279
O .022
22.3C4
2
1CC-CH
0. 178
0 . 245
2.322
2 .228
11. 076
0. 1 96
0 .232
2
-------
TABLE D-3. WATER QUALITY RESPONSE TO PRACTICES FOR RIDGE SOIL
10
VD
PRACTICE! i
*Variai>l«s2CC-CV
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
0.064
2.049
11 .065
10.729
18.460
0.139
1 .107
95.22?
0.408
0.0
0.969
1 .376
7.060
0 .046
0.034
0.953
0.111
0.41C
1.514
1 .097
3.232
2.272
55.26ft
15.491
73.231
O .014
23.562
2
2CC-CH
O.C42
0.927
5.360
_ 5.191
13.460
0.131
0.536
46.74B
0.403
0 .31*
0.590
1 .306
7.030
O.C63
O.C18
0.510
0.112
0.542
1 .205
1 .378
1.906
2.372
39.643
13.269
55.294
0 .Gl«
24.1 33
3
2CC-NT
0.019
0. 537
3.262
3.145
21 .930
0. 157
0.326
29.035
C. 337
0.623
0.552
1.568
7.663
0.069
C.012
0. 313
0.124
0.706
1.202
1.390
1.411
a..i9j._
27.732
12.595
42. 518
0.024
20.051
4
2C3-CV
0. 064
2. 09S
11. 3C7
1 0. 964
1 3. 060
0. 136
1.131
97.175
C.4C1
0.0
0.956
1. 35*
5.956
0.045
O.C24
C.971
0. IOC
0. 398
1.510
1 . 100
3. 254
2.920
57.010
15.536
75.365
0.013
ze.eie
5
2CD-CI-
O.C42
1.171
6.621
6.415
13.06C
C.125
0.663
57.313
0.2*7
C.2CC
1 .246
5. 556
0.055
C.022
0.612
O.OSC
1.222
1.35E
2.143
45.864
12.619
62.323
0.01C_
26. f.f c
6
2C3-NT
C. 019
C.876
5.1C3
4. 931
14. 740
C. 139
C. 51C
44.1 as
C.362
C. 360
C.672
1.394
6. 343
C.069
C.017
C.469
Ci 076
C. 577
1.1 ei
1.4C6
2. 648
12.025
52.032
C. C19
25. 1 24
7
2CCWH
O.C22
0.332
2.102
2.C22
6.70J
C.089
0.21 C
1R.63S
C.380
C.224
0.299
C.89-3
4.727
0.056
C.003
C.226
0.076
0.495
C.C37
1.982
0.948
3.553
45.G52
12.023
6C.62"
0.016
1 7.120
6 9
2C6*I — NT2CC-CVT
0.013
0.210
1.375
1.321
8.700
O.C84
0. 139
12.323
0.358
0.264
0.223
0.844
4.727
0.056
O.OC5
0.154
0.06C
0.500
0.754
2.2C1
0.682
3.553
44.689
1 1.713
59.959
0.017
1 7.367
C.C64
1 .455
7.623
17.030
C.I 18
c.soe
70.C09
C.C
0.754
1 .175
6.623
C.047
C.026
0.725
0.123
0.423
1 .312
1 .266
2.5d5
2.461
54.C37
14.321
7C.870
0.014
2J.322
10
2CC-CHT
O.C42
c .6se
3.927
3.769
1 7.060
C .121
C.393
34.7C5
C .416
C.332
0.463
1 .2 10
6 .623
C.065
0 .014
C .J9C
C.126
0.559
1 .116
1 .466
1 .589
2.461
36.350
12.630
53.641
O.C19
£C .652
11
2CB-NTT
C.019
0.623
3.739
3.607
14.040
0. 132
0.374
32.759
0.363
0.428
J .326
1.323
6.186
0.073
0.013
0.374
0.092
0.603
1 .109
1 .496
1 .439
2.715
34.41i!
12.632
*9.759
0.020
21.534
for Variable Definitions.
-------
TABLE D-4. HATER QUALITY RESPONSE TO PRACTICES FOR UPLAND SOIL
10
PRACTICE:
•Variable
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
1
3CC-CV
C.127
S.9S3
33.927
12.560
O.995
3.393
290.040
0 .325
O.O
0.625
C.950
S.833
O .013
0.115
3.252
0.15?
3.556
0.467
10.274
2.679
233.870
50.000
236.749
0.00?
12.935
2
3CC-CH
0.105
15.567
15.315
12.560
C.C9S
1 .587
136.599
0.363
0.256
0.375
0.993
3
3CC-NT
C.033
1.559
9.430
9.091
15.600
0. 147
4
3CB-CV
0.127
6.095
34.7C1
33.529
9.472
O.oee
0.9*3 3.470
S2.551 296. SOP
C.516
C.4<54
0.472
1.472
5.833 6.526
0,026 0.054
0 .055
1.565
0. 166
Q_.2_37_
2.009
0.827
5.233
2.679
102.967
20.730
126.577
O.OOB
0.034
0.959
0.241
0.459
] .699
0.977
3.639
2.573
46.432
16.331
65.335
0.015
6. 355
0. 307
0.575
4.974
0.0 12
0. 1 17
3.324
0. 148
0. 094
3.606
0.46C
10. 4£fi
3.377
264.661
50.000
318. C37
13.45?
5
3CB-0
C.1C5
3.4C2
19.62?
19.155
9.472
C.089
l^C.137
C.332
C.162
0. 392
c.aee
4.974
0.02C
c. cee
1 .937
0.144
0. 1 75
2.29C
0.722
6.263
3.377
1 42.02C
25.2C1
171 .596
0.006
13.712
6
3C3-NT
C.C63
2.5 = 1
1 5. C69
14.543
1C.46C
0. 1 2S
1.507
125.991
C.427
C.32C
C.542
1.29C
5.275
C. C35
C. C53
1.491
C.i£3
C.TC9
2.022
C.621
5. 061
3.1 84
74.052
2C.144
57.360
C.01C
1 c.15?
7
C.C76
C.964
£.933
5.761
6.512
C.071
0.593
52.21 1
0.366
0.185
0. 162
C.712
3.935
Q..033
O.022
C.628
0. 129
0.307
1. 104
1.503
2.31 1
4.269
79.441
1 3.877
97.537
C.C10
S.552
e 9 ic
3CCWI — M3CC-CVT 3CC-CHT
C.C72
_c_._6_fiS_
3.835
2.736
6.832
O.O77
0.389
34.620
0.409
C.22C
J.137
0.766
4.061
0.042
3.015
0.421
0. l£3
0.379
C.993
1.672
1 .762
4.137
61 .934
1 3.066
79.137
0.013
3.823
C.127
0.227
24.426
23.591
1 1 .456
0.078
2.443
209.338
0.326
O.C
C .462
0.778
5.547
0.014
C.034
2.366
C.164
0.123
2.693
C.616
7.630
3.C26
2C6.485
34.843
244.356
C.CC4
C.JCE
1.912
11 .447
1 1 .C41
1 1 .456
o.crsi
1 .145
99. 1O3
0.264
C ,c7C
o.a?2
0 .907
5.547
C.C3C
C .041
i . iec
C.173
0 .275
1 .C13
4 .008
3.C26
ee./36
17.173
1C8.937
C.CC9
10.1 £6
11
3CU-NTT
O.C83
1.611
10.973
10.487
9.936
0.116
I . 087
94.442
0.429
C.340
C.395
1 .164
5.115
0.040
0.039
1 .095
0.194
0.351
1 .690
0.988
3.903
3.283
65.279
16.939
85.501
0.012
8.552
* See Table D-l for Variable Definitions.
-------
Additional Interpretations
In Section 2 the primary implications of the results are discussed.
Of particular interest is the apparent attenuation of the effects of
erosion control on water quality, as the analysis moves downstream from
the river into the impoundment and when components other than sediment
are considered. A possible conflict between the water quality manage-
ment goals of controlling both sedimentation and eutrophication using
these types of practices has also been discussed in Section 2. Additional
interpretations of the impacts of the various practices and soil types
on eutrophication can be derived from Figs. D_i^ D_2r and D-3.
In Fig. D-l, the three components of phosphorus loading (available
particulate, soluble, and crop residue) are depicted for each soil type
and practice. The importance of residue phosphorus leached by snowmelt
is apparent in the practices involving reduced tillage, despite the fact
that leaching of only 1 percent of the available residue phosphorus has
been assumed. As noted in Appendix B, laboratory studies suggest that
one freezing-thawing-leaching cycle could release from 5 to 28 percent
of the phosphorus in various crop residues. The importance of the soluble
phosphorus component is apparent in the relatively flat, phosphorus-rich,
lowland soils. In general, impacts of the various practices on avail-
able phosphorus loadings are considerably different (in magnitude
and often in sign) from the impacts on soil loss.
The components of the mean summer light extinction coefficients in
the impoundment are displayed for the different practices and soil types
in Fig. D-2. Extinction coefficients are inversely proportional to Secchi
297
-------
r o
M
I 3
o»
O
z 2
Q
1
to
CC
O
I
Q. 0
CO w
O
UJ
_l
CD
<
I
LOWLAND SOIL
Component
RIDGE SOIL
UPLAND SOIL
1 -
Practice: 1 2 3 4 5 V6 7 8 9 10 11
75
50
CM
25
UJ
i
O °°
u QC
UJ
75 b
25 x
UJ
CO
0 §
75 i£
O
a.
50
UJ
_J
CD
»i
Figure D-d .
Coaponents of Available Phosphorus Loading for Different
Soil Types and Practices
298
-------
300
200
(~ 100
10
£
o
c.
o
2 300
o
z
&
(O
UJ
a:
200
100
100
Pract
LOWLAND SOIL
RIDGE SOIL
^Nitrogen
,Phosphorus
rLight
.003
.005
.01
.02
.04
.003
.005
.01
.02
.04
.003
o
.005
.01
.02
.04
Figure D-3. Components of Algal Growth Resistance for
Different Soil Types and Practices
299
-------
disc transparencies, which are noted on the right-hand scales of Fig. D-2.
Dissolved color and algae are primarily responsible for light extinction
in the case of the flat, poorly-drained, lowland soils, which are also
relatively high in phosphorus and organic matter. For the relatively
erodible and phosphorus-deficient upland soils, turbidity (attributed
to non-algal suspended solids) is primarily responsible for light
extinction. Erosion controls cause substantial (up to 4-fold) increases
in water transparency only in the upland soil case. In the other cases,
algal growth and color tend to reduce the relative impacts of erosion
controls on transparency.
Fig. D-2 depicts the limiting effects of light, phosphorus, and
nitrogen on impoundment algal growth for each soil type and practice.
According to the model used to predict chlorophyll-a concentrations, the
total resistance to algal growth is computed as the sum of the resistances
attributed to light, phosphorus, and nitrogen. The inverse of this sum is
a measure of the potential chlorophyll-a concentration, as depicted on
the right-hand scales of Fig. D-3. In general, phosphorus is the most
important controlling factor in all cases examined, while nitrogen is
generally insignificant. The relatively high degree of phosphorus re-
sistance in the upland soil cases reflect the effects of (1) the low
phosphorus contents of those soils and (2) their relatively high erosion
rates, which tend to increase the phosphorus trapping efficiency of the
impoundment because of the influence of sedimentation on phosphorus
settling velocity (see Appendix C ). In the upland soils, erosion
controls generally cause less resistance to downstream algal growth both
300
-------
with regard to phosphorus and to light. In the cases of lowland and
ridge soils,, however, chlorbphyll-a levels are not influenced
substantially by the practices examined.
These results indicate the relative impacts of these agricultural
practices on impoundment eutrophication are small, except in the
extreme upland soil case, in which a 10-fold decrease in soil loss results
in a 4-fold increase in algal biomass (comparing practices CB-CV and CBWH-NT)
These conclusions primarily result from the following factors:
(1) a generally small fraction (5 to 10) of the particulate phos-
phorus in soils is biologically available (acid extractable);
(2) reduced tillage alternatives create a potential for leaching
of phosphorus from crop residues during snowmelt periods and
cause enrichment in surface soil phosphorus levels;
(3) the phosphorus trapping efficiency of an impoundment appears
to be a strong positive function of sedimentation rate; and
(4) algal growth is sensitive to available light and is therefore
stimulated by reductions in ambient turbidity levels.
An improved picture of the effects of erosion controls and other agri-
cultural practices on impoundment eutrophication could be derived by
obtaining more accurate, quantitative definitions of the above relation-
ships through additional data compilation and analysis. Interpretation
of the water quality effects of eutrophication could be enhanced by
expanding the impoundment model to permit direct estimation of dissolved
oxygen levels, as influenced by external and internal sources of oxygen
demand.
301
-------
D-8
123456789 10 11
0
Practice:
T!
fl)
w
(U
4-1
0)
H
0)
M-l
0) O
c -H
8
a
A
-------
Sensitivity analysis
One of the advantages of utilizing a framework of relatively simple
models for evaluating water quality impacts is that it facilitates sen-
sitivity and error analyses. These help to identify key structural or
parametric assumptions, as well as guide further model development
by indicating the most fruitful areas for investment of additional data
collection and analytical resources. For a given total investment, the
"most fruitful" area for further work would be that which results in the
greatest degree of improvement in the accuracy of the model or model
framework. Specific strategies for implementing sensitivity and error
analyses have been discussed in detail by Thomas (1965) and Walker (1977) .
As model complexity increases, the size, expense of implementation, and
increasing effects of data errors tend to reduce both the feasibility
and the benefits of performing these types of analyses.
Relatively crude, initial applications of these methods to the water
quality model framework developed and applied in this project are des-
cribed below. They demonstrate the feasibility and benefits of conducting
sensitivity and error analyses within our model framework. This means
that they indicate those components within the model framework which are
most important to evaluating both the absolute and the relative impacts
of these agricultural practices on water quality.
At a basic level, a marginal sensitivity analysis would involve
evaluating and ranking the first partial derivatives of the predicted
variables with respect to the parameter estimates:
303
-------
S±.k = 9k. ^L. = 6 in YJJ (1)
' Yij S6k 6 in 6kJ
where, sijk = sensitivity coefficient for predicted variable i,
case j, and parameter k
6 = nominal value of parameter k
Jc
y. , = nominal value of predicted variable i for case j
Defined in this way, a sensitivity coefficient equals the percent increase
in the predicted variable resulting from a 1 percent increase in a given
parameter value. While these derivatives can be evaluated analytically
for simple models, finite-difference methods are usually easier to implement
if the model is computerized. For a given case (e.g., soil type/
agricultural practice combination) and variable, the parameters can be
ranked according to decreasing absolute values of the sensitivity co-
efficients. This provides a preliminary indication of which parameters
or processes are most important in determining the prediction.
This strategy has been implemented for a total of 12 predicted
variables, 33 cases (3 soil types x 11 practices), and 38 parameters.
The parameters, which characterize the various processes represented in
the watershed and impoundment models, are listed in Table D-5 along with
their nominal values and equation references. To illustrate the method-
ology, results are presented and discussed below for 2 predicted
variables and 9 cases (3 soil types x 3 practices).
The ranked sensitivity coefficients for the five most critical
parameters in each case are presented in Tables D-6 and D"-7 for predictions
of impoundment light extinction coefficients and chlorophyll-a levels,
304
-------
TABLE D-5. PARAMETERS INCLUDED IN SENSITIVITY ANALYSIS
Watershed Model (Appendix B)
Symbol Value Equation
R 160
Ki .50
K2 20.
K3 2.
dCL 1"6?
dgj 1.00
dSA -33
Ktt .34
K5 .20
q .25
q° .178
^ .064
.127 *
Ke 2.0
C .03
D
Y 1.0 *
P 1.0
.50
Ky .01
., .
.6
Ke .5
Yc 10.
(1)
(3)
(4)
(6)
(7)
(8)
(9)
(10)
(11)
(13)
(14)
(17)
(23)
(24)
(26)
(29)
(31)
(33)
Impoundment Model (Appendix C)
Symbol Value Equation
K clay 50
s
silt 120
sand 8000
ao -377
ai -.779
a2 .222
as 0.0
ai+ 1.201
c .223
o
ci -.445
c2 .351
c3 .862
c^ 0.0
e .04
w
ks .085
kB 30.
KG 6.0
PCS 3 . 0
KL -44
fL 1.363
fp 1 . 866
f 1.866
n
(3)
(11)
(11)
(11)
(11)
(11)
(23)
(23)
(23)
(23)
(23)
(27)
(28)
(29)
(33)
(39)
(40)
(51)
(65)
(65)
(65)
Parameter values for lowland, ridge, and upland soils, respectively.
305
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TABLE D-6. EXTINCTION COEFFICIENT SENSITIVITIES*
Soil Type Rank
Lowland 1
2
3
4
5
Ridge 1
2
3
4
5
Upland 1
2
3
4
5
(1 CC-CV)
Param. Sens.
Fcs -55
q° -37
K5 -.31
K4 .29
k .28
S
F -.67
cs
K5 -.66
\ -63
ks '61
ait -.46
K5 -.95
Fcs -95
k .91
s
Kit -91
R .84
Practice
(5 CB-CH)
Param. Sens.
F -.49
cs
q°
Yc -'31
kB '28
fp -.24
F -.55
cs
K5 -.53
K4 -50
k .48
S
ait -.46
F -.88
cs
K5 -.85
ks .83
Kit -80
R .73
(7
Param.
o
qr
F
cs
kB
Yc
q
kB
p
F
cs
KS
ait
F
cs
k
s
K5
Kit
R
CBWM)
Sens.
.51
-.42
.36
-.35
-.31
.47
-.44
-.35
-.35
-.33
-.67
.57
-.54
.51
.43
* A sensitivity coefficient represents the percent increase in the
predicted value resulting from a 1 percent increase in the res-
pective parameter.
306
-------
respectively. For the extinction coefficients, Tables D-6 indicates the
importance of the assumed ratio of summer-average to mean-annual
TABLE D-7. CHLOROPHYLL-A SENSITIVITIES *
Practice
Soil Type Rank
Lowland 1
2
3
4
5
Ridge 1
2
3
4
5
Upland 1
2
3
4
5
1 (CC-CV)
Param. Sens.
ait —.75
f -.59
P
ai .54
*£ *48
fL -.34
ait -1.68
ai .96
f -.75
P
a -.53
o
KL '30
ait -3.83
ai 1.61
a -.89
o
f -.81
P
K5 .32
5 (CB-CH )
Param. Sens.
f -.56
P
ait --50
K .46
L
ax .42
£L
a4 -1.17
ai .77
f -.73
P
a -.43
o
K .27
ait -2.81
ai 1.36
f -.83
P
a -.75
o
KS • 56
7 (CBWM)
Param. Sens.
f -.58
P
Yp -39
K. .37
L
q -.37
a .32
1
f -.74
P
a^ =.57
a! .52
q -.31
a -.29
o
ait -1.25
Si\ . 86
f -.81
P
a -.48
o
CD .26
* A sensitivity coefficient represents the percent increase in the
predicted value resulting from a 1 percent increase in the res-
pective parameter.
307
-------
suspended solids and color concentrations (F ) , delivery ratio parameters
cs
(K and K ) and the slope of the extinction coefficient versus suspended
solids concentration (kc) . Sensitivity rankings vary somewhat with soil
O
type and practice. For example q° and y~ appear to be important .only in
K c
the lowland soil, which has a relatively high color contribution. The
chlorophyll-a sensitivity rankings suggestion the importance of the
phosphorus trapping parameters (a., a , a ) and the parameters of the biomass
model (f , K , f ). The listing of only five parameter sensitivity co-
P L L
efficients for each case does not imply that the remaining should be
ignored, but serves here as an illustration.
A modification of the above procedure has been implemented by
estimating the sensitivities of the relative magnitudes of the predicted
variables to the assumed parameter values. Relative sensitivity
coefficients are of the form :
Yij 89k Yio 69k
= smtY.-A^)
6 In 9
The relative magnitude of any predicted variable is defined as Y../Yj_»
ratio of the value for a given case to the value for an assumed base case
A sensitivity coefficient evaluated as prescribed above represents the
percent increase in that ratio resulting from a 1 percent increase in a
given parameter value. When the model framework is being used to
compare practices, these relative sensitivity coefficients are perhaps
308
-------
more important to consider than are the absolute versions. Parameters
have been ranked according to this scheme using practice 1 (continuous
corn with conventional tillage) as a base case for each soil type.
Results are presented in Tables D-8 and D-9 for predictions of extinction
coefficients and chlorophyll-a levels, respectively. In comparing these
TART.K D-8. EXTINCTION COEFFICIENT SENSITIVITY* REIATIVE TO PRACTICE 1
Soil Type Rank
Lowland 1
2
3
4
5
Ridge 1
2
3
4
5
Upland 1
2
3
4
5
Practice
5
Parameter
K5
K
it
k
s
R
dCL
K
5
k
S
F
CS
K
4
R
R
K
S
K
i*
a
k
S
(CB-CH)
Sensitivity
.11
-.10
-.10
-.10
-.09
.13
-.13
.13
-.12
-.12
-.11
.11
-.10
-.10
-.08
7
Parameter
K5
K
**
k
s
R
dCL
ks
F
CS
K
5
K
1*
kB
K
5
R
K
"*
ks
F
CS
(CBWM)
Sensitivity
.22
-.21
-.21
-.18
-.18
-.35
.33
.31
-.30
.26
.41
-.41
-.39
-.35
.28
* A sensitivity coefficient represents the percent increase in the predicted
value resulting from a 1% increase in the respective parameter.
309
-------
results with those in Tables D-6 and D-7, two general observations can be made
First, the lists of most important parameters change somewhat as the
ranking criteria switches from absolute to relative sensitivities.
Secondly, the relative sensitivity coefficients are generally lower in
scale. This essentially reflects that the model framework is more
TABLE D-9. CHLOROPHYLL-A SENSITIVITY* RELATIVE TO PRACTICE 1
Soil Type Rank
Lowland 1
2
3
4
5
Ridge 1
2
3
4
5
Upland 1
2
3
4
5
Practice
5
Parameter
a
if
a
1
a
o
K7
FD
a4
a,
1
K_
7
a
o
d
SI
a4
KL
a.
1
K
5
K
4
(CB-CH)
Sensitivity
.25
-.12
.07
.06
-.04
.52
-.19
.12
.10
.07
1.02
.33
-.25
.24
-.23
7 (CBWM)
Parameter Sensitivity
a .46
"*
a1 -.22
1
a .12
o
K -.11
L
q -.11
a4 1'12
an -.44
1
a .24
o
^ .20
d .16
SI
a4 2.58
a -.76
a .42
0
K .23
7
K. .18
L
* A sensitivity coefficient represents the percent increase in the
value resulting from a 1% increase in the respective parameter.
predicted
310
-------
accurate for estimating the relative impacts of the various practices
than for estimating the absolute impacts.
For estimating extinction coefficients in a relative sense, the
most important parameters appear to be those related to sediment delivery
(Kg, K , dCL)f rainfall erosivity (R) , and suspended solids light
extinction (kg). Note that FCS is considerably less important here,
than when the parameters are ranked according to absolute sensitivities
(Table D-6). This suggests that a given percent error in the estimate of
this parameter would have a nearly constant percentage impact on the
computed values of the light extinction coefficients for the various
practices. This impact is subtracted out when relative sensitivities
are considered. In evaluating relative chlorophyll-a levels (Table D-9),
the phosphorus trapping parameters appear to be most important, along
with the leached fraction of crop residue phosphorus, K?.
Based upon interpretations; of the results of the above sensitivity
analyses, the most important parameters and processes for estimating the
relative impacts of agricultural practices according to various criteria
are summarized in Table D-10, The sensitivity rankings are typical of the
various soil types and practices considered. They provide tentative
indications of the most important areas for future model improvement.
At a higher level of sophistication, parameters could be ranked based
upon their respective contributions to the total variance of predictions
derived from the model. Such an error analysis could alter, somewhat,
the rankings presented in Table D-10, The merits of such an analysis
should be explored in follow-up work.
311
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TABLE D-10. SUMMARY OF MOST IMPORTANT MODEL PARAMETERS FOR ESTIMATING
THE RELATIVE WATER QUALITY IMPACTS OF VARIOUS AGRICULTURAL PRACTICES
Criteria
Parameters
Processes
River Sediment Concentration &
Impoundment Sedimentation
CL'
K
K
sediment delivery
texture enrichment
River Phosphorus Concentration &
Impoundment Phosphorus
Loading
VK4
R
sediment delivery
residue leaching
rainfall erosivity/
gross erosion
Impoundment Nitrogen Concentration C , C , C.
q
F
D
nitrogen trapping
total flow
denitrification
River Light Extinction Coefficient d , d , K_, K.
R
sediment delivery
texture enrichment
rainfall erosivity/
gross erosion
solids light extinction
Impoundment Phosphorus
Concentration
Impoundment Sediment
Concentration
Inpoundment Color Concentration
Inpoundment Light
Extinction Coefficient
Chlorophyll-a
V V al
dsr dcL
Ks
*8
I\^ f I\—
V V dCL
*s
Fcs
a4' ao'al
Kr
phosphorus trapping
residue leaching
sediment delivery
sediment trapping
soil organic
natter enrichment
texture enrichment
sediment delivery
solids light extinction
seasonal variations in
color and suspended solids
concentrations
phosphorus trapping
residue leaching
algal growth
312
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REFERENCES, APPENDIX D
Thomas, H.A., Jr. "Operations Research in Disposal of Liquid Radioactive
Wastes in Streams", Harvard Water Resources Group, Cambridge, MA,
Dec. 1965.
Walker, W.W., Jr. "Some Analytical Methods Applied to Lake Water Quality
Problems" Ph.d. Thesis, Engineering, Harvard University, (University
Microfilms, Ann Arbor, MI), 1977.
313
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Appendix E
Discussion of Benefit Estimation
This appendix presents the results of the literature review and work
on benefit estimation. The discussion follows the outline described in
Table E-l.
Introduction
We begin by emphasizing several points frequently made. As is gen-
erally agreed upon among economists willingness-to-pay is the appropriate
measure of benefits. The choice facing society is not between clear water
and polluted water, for example, but between various levels of pollution.
It is the incremental or marginal values that are important in making
decisions. The "demand" for water quality (the analog to market demand) is
the aggregate of how much individuals will give up (will pay) to enjoy
additional increments of improved water quality.
The economic theory for valuing benefits is well developed. A com-
plete theory on the provision and use of public goods, those which are
enjoyed in common, such as the water quality of a stream, has been developed.
From the literature of welfare economics we get such concepts as the
Pareto Optimum criteria, consumer surplus, the social welfare function,
314
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Table E-l
Outline of Benefit Estimation Discussion
1. Points from proposal
A. Willingness to Pay — Appropriate Measure
B. Economic Theory Well Developed
C. Not so Easily Applied
1) Lack of Market
2) Problem of "Intangibles"
3) Thorough Analysis Impossible
4) Data Needs Immense
5) Equity Question
2. EPA Needs (Our Impression)
A. Further Pollution Control Expenditures Assessed
on Basis of Benefits
B. Generally Accepted Methodology
1. EIS Review
2. Support Regulatory Standards
C. Policy Direction
3. Criteria
A. Ease of Application (Data)
B. Identified Pollutants
C. Theoretical Validity
D. Pollutant -^ Environmental (Water) Quality
Value Measurement
E. Benefit Quantification
F. Distribution of Impacts
G. Generalizability
4. Examples
315
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and the equi-marginal principle for selecting the appropriate level of
pollution abatement.
But as is well known, these general principles for management of public
goods are not so easily applied. The problems of the misallocations of
resources and externalities are not theoretical but empirical ones.
For instance, there is the problem of the lack of a market. As we said,
public goods are enjoyed in common. They are shared, so they are not
contained in market transactions and they have no market price to use
to define demand. The question of intangible benefits is also complex.
A hypothetical demand curve can be derived from aggregating individuals'
willingess-to-pay (for increased increments of a public good, as mentioned
above). One approach to estimating willingness-to-pay is to calculate
the damages that would occur if a project were not undertaken. However,
this method still underestimates psychic benefits (called "intangibles").
In most cases a complete, thorough analysis is impossible because it
is too difficult to estimate the multitude of impacts of, say, a change
in water quality even though it is said (by Kneese and others) that a
materials balance concept should be used. The existence of interactions,
substitutions and indirect benefits in most water quality control prob-
lems contributes to the difficulty of conducting an adequate analysis as
defined by economic theory. Furthermore, data needs are immense and the
expense and personnel necessary for data collection are great. These
are the greatest impediments to good empirical benefit estimation work.
Examples of the types of data used for the various methods of estimating
316
-------
water quality benefits are survey data, property sales prices, detailed
studies of physical damages, and origin and destination data from travel-
lers. Many methods use data that must be collected anew for each study.
In addition to these obstacles there is the equity question. Environ-
mental control measures are inherently redistributive and there is no
generally accepted method for the resolution of the conflict of interest
among those who gain and those who lose from environmental quality
improvement. This issue is addressed in Section 6 of the report.
Need for Benefit Estimation
Prom discussions with personnel in EPA and review of the literature
including the study of water reuse and benefit estimation done by ERCO
for the EPA (1977). The need for benefit estimation can be summarized
as follows.
• A time may come when the national (or industry-specific) pollution
control effort will reach a point at which further expenditures
must be assessed on the basis of benefits received.
• There is a need to develop a generally accepted methodology for
estimating project benefits; something straightforward and applica-
ble to multiple situations including review of EIS reports.
• Regulatory standards may need to be supported by benefit estimation.
Criteria for Benefit Estimation Methods.
Meeting these needs will be a difficult task. To assist in evaluating
methods of benefit estimation we developed a set of criteria which define
a "satisfactory" benefit estimation framework:
317
-------
A. Ease of application (availability of data).
Does the methodology rely on data generally available, such as
the census and property value assessments or must it be collected
systematically each time?
B. Consideration of identified pollutants.
This criteria is necessary to relate the benefit estimation to
non-point source pollution control in general and, specifically,
to apply it to particular management practices.
C. Theoretical validity.
This necessity was covered earlier in our discussion of willing-
ness-to-pay. In practice, it usually means development of a
demand function rather than estimation of gross benefits or use
of a "judgment value" for benefits.
D. Investigation of the relationship between pollution levels and
value measurement.
The reasoning behind this requirement is the same as for B above,
to be specific.
E. Quantification of benefits.
To compare with marginal costs we must be able to discuss incre-
mental benefits. We must have some measure of benefits to make
a decision — they are not infinite.
F. Identification of distribution of impacts.
This criteria concerns the equity question. We must know who
gains, who loses, and the consequences of alternative controls
to facilitate a decision. This is not necessary to insure
national economic efficiency but it certainly is recognized as
important. (See, for example, the hearings on the Principles
and Standards in response to the President's concern.)
G. Generalizability of methodology.
For it to be useful to meet EPA needs, the technique must not
be limited to a single problem or region.
Our assessment or benefit methodologies may show that certain techniques
appear more promising than others for specific pollutants or impact
groups or land/water configurations.
318
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Examination of Examples of Benefit Studies
Having reviewed over thirty recent benefit studies we have selected
eight representative to examine in detail in light of the above set of
criteria.
1. Dennis P. Tihansky, "Damage Assessment of Household Water Quality,"
Journal of the Environmental Engineering Division, ASCE, Vol. 100,
No. EE4, August 1974.
This paper develops a comprehensive framework for analysis of
national mineralized water supply damages. The aggregate mineral content
of water, i.e., the total dissolved solids (TDS), increases the depreci-
ation rate of household items and adds to their maintenance needs.
Tihansky derives functions relating these impacts on households to various
levels of dissolved mineral constitutents in the water supply. Data from
household surveys are used to derive damage relations comparing the
average service life of twenty household items to TDS. For example, the
average life span of toilet facilities decreases exponentially as the TDS
content in water supply increases.
Tihansky defines monetary impacts as the sum of annualized capital
costs plus operation, maintenance and repair (OMR) expenses. Total
household damages in monetary terms are calculated from the individual
household item damage equations (TDS and hardness versus dollars).
Tihansky applies these damage functions to state-by-state household
statistics, such as income levels, and data on water quality from
USGS and municipal water supply surveys. This yields regional estimates
of damages, expressed as intervals to account for variability among
households and to reflect water quality sampling errors.
319
-------
Significant impacts occur in the midwest and southwest. The
least impact is in the south, northwest and New England. The mean per
household for the United States is $33.50 per year.
The final step of the analysis consists of the calculation of
the percent of damage caused by man-made as compared to natural sources
of TDS load. Tihansky uses a generalized estimate of approximately
thirty percent, derived from a study of the Colorado River and another
of a New Jersey river.
Tihansky"s analysis meets all our criteria. For data he relies
on existing studies relating TDS to household item damages (A). He
treats a specific pollutant (B). He develops functional relationships
between damages and pollutant (C). The relationship between value
measurement and levels of pollution is explicit (D). Benefits are
quantified in dollars (E). The distributional aspects are addressed
in terms of the differences in impacts among states and regions in the
United States (F). His methodology is general enough to be applied
to state and regional data (G).
2. Sharon Oster, Survey Results on the Benefits of Water Pollution
Abatement in the Merrimack River Basin, Department of Economics.
Yale University, September 1976. Also in Water Resources Research,
October 1977.
The report deals with the estimate of benefits of water quality
improvement derived from a frequency of use/willingness-to-pay survey
conducted in 1974 in the Merrimack River Basin. The study consisted of
a telephone survey of 200 residents of towns along the river. The
questionnaire requested information on willingness to be taxed or to
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pay a yearly charge for the river to be cleaned up. It also asked for
information on increased use of the river for recreation activities if
it were cleaned up.
The results of the survey showed that the average aggregate willing-
ness-to-pay for river clean-up is slightly over $12.00 per year. The
mean increased use of a clean river is thirteen days per year. This
is a willingness-to-pay measure for a complete river clean-up.
Oster analyzed the survey results by cross-tabulating income with
willingness-to-pay data and with increased use. She found that both
increased with increased income.
Oster's study meets criteria E, F and G. Benefits are quantified in
two ways, dollars and recreation activity days (E). The equity question
is explicitly addressed in terms of differences in willingness-to-pay
of different income groups (F). The method of benefit calculation is
generalizable, although the data would have to be collected for each
study area (G).
Critera A, B, C and D are not met. As explained above, a survey must
be conducted each time the methodology is to be applied (A). Oster does
not specify pollutants (B), she asks about payment to "clean up" the
river. This is ambiguous. An alternative approach was used by Gramlich
in his thesis on the Charles River (Harvard University, March 1975) who
uses a more theoretical questioning technique, posing levels of clean
water corresponding to standards for, for example, "swimmable" quality
water. Although she investigates willingness-to-pay, Oster does not
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develop a functional relationship between willingness-to-pay and alternative
water quality levels (C). Oster also does not specify a relationship between
pollution, water quality and personal utility (D); she considers total
utility for a total clean-up (undefined).
3. J. C. Day and J. R. Gilpin, "The Impact of Man-Made Lakes on Residen-
tial Property Values: A Case Study and Methodological Exploration,"
Water Resources Research, Vol. 10, No. 1, February 1974.
This study does not concern water quality impacts. However, it
does explore certain methodologies that may be important for assessing
the benefits of water pollution control. The analysis uses a market
study method and a survey method to investigate the benefits of develop-
ment of a reservoir on nearby property values.
Data are collected for 455 single family and apartment houses
surrounding the project area. A regression analysis is performed to
determine the factors associated with residential assessed property
values (sales values would have been more meaningful, the authors
contend, but only a small number of records were available). Distance
from the reservoir predicted only 0.8 percent of the variation. Day
and Gilpin feel that this result suggests that the reservoir project
had not influenced assessed property values; so they tried an alter-
native approach, behavior analysis.
A survey was conducted of 35 percent of the dwelling units surround-
ing the project area to determine residents' perceptions of the value
of the reservoir. Ninety-four percent did not know about the project
when they moved to the area. The questionnaire requested interviewees
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to rank the factors which contributed to the benefit of living in the
study area. Only two percent ranked the reservoir in their top four
choices and these people lived adjacent to the project area. Seventy-
one percent of those interviewed felt that the reservoir project did
not affect property values. Day and Gilpin conclude that benefits are
restricted to a small area contiguous to the lake property.
Since this study uses two methodologies, they will each be assessed
in light of our criteria. The market study meets criteria A, C, E
and G. The survey methods meets only F and G. The market study approach
is appealing because it uses generally available data, land value assess-
ments (A). The survey method, as in the Oster study, has to be repeated
each time it is used. Regression analysis is a theoretically valid ap-
proach (C). The behavior analysis methodology is qualitative and there-
fore not theoretically valid. It could, however, be a helpful complement
to a more rigorous method. The market study quantifies benefits (E).
The survey does not, although benefits of the reservoir are compared
to other benefits through ranking. The property value study does not
address the equity question although it could be used to do so. The
questionnaire, however, does show that certain benefits accrue only to
those living adjacent to the water body (F). The property value method-
ology is generalizable (G). So are the survey and ranking analysis
methodologies but they must be repeated each time.
Neither methodology meets criteria B or D since the study was
not concerned with water quality, although they could be adapted to
study water quality impacts. In particular, the behavior analysis
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methodology might be used to investigate the relationship between
water quality and a value measurement.
4. Dow Chemical Company, An Economic Analysis of Erosion and Sediment
Control Methods for Watersheds Undergoing Urbanization, Final Report,
Midland, Michigan, February 1972.
This is one of the few analyses specifically concerned with sediment
as a water quality determinant. The study relies on available cost data
relating to sediment damages and presents average damage costs per ton
of sediment entering the stream system. It is part of a larger report
focusing on soil losses from urban construction sites which analyzes
the cost and effectiveness of numerous sediment control systems. The
economic impact of sediment in water was estimated for the Potomac River
below the confluence with Seneca Creek.
The study assumes that a reduction of a unit of sediment provides
a proportional reduction of cost, an assumption which probably holds for
large scale sediment removal but does not apply to small reductions.
From measurement of the existing total sediment transport in the river,
a reduction in yearly average sediment load was estimated for the river
to be considered "clear." This amount was used to reduce annual dollar
damage estimates to dollars per ton of sediment removed.
Damages per ton of sediment to downstream water bodies are calculated
in terms of uses which are defined as: metropolitan water supply;
industry including electric power, dredging and commercial fishing;
recreation including fishing and boating; aesthetics; and flood damage
abatement benefits due to sediment control impoundments. Calculation
methods are as follows:
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Metropolitan Water Supply
The difference is calculated between chemical treatment costs,
assuming the water is clear and existing treatment costs. Costs are
linear versus sediment concentration so cost differences are divided
by required reduction in sediment per year to give cost per ton of
sediment removed.
Electric Power Improved cooling condenser design prevents plugging
from fine particles so cost is not reduced by lower sediment concentrations.
Dredging From available data a cost per cubic yard for dredging is
developed which includes disposal costs. This is multiplied by the
past average amount dredged and divided by the required reduction in
sediment per year.
Commercial Fishing The present dockside value of fish and shellfish
is calculated. From data on the impact of suspended solids on trout
and shellfish density as a percent of normal for "clean" streams,
the increase of commercial catches is calculated assuming that it
would increase proportionately to the fish population. The increase
per ton of sediment is then determined.
Recreational Fishing An average number of fishing days is estimated
from Fish and Wildlife Service forecasts and an average value per
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man-day for fishing is assumed. The average annual value of all
fishing days in the area is calculated and the increase in value is
calculated assuming the same fish density increases with reduction
in sediment as for commercial fishing. Value returned per ton
of sediment is determined.
Boating The number of pleasure boats using the tidal Potomac is esti-
mated and annual total recreation expenses are calculated on the
basis of amortization of an assumed average original cost and annual
expenditures per boat. A percentage increase in boating due to clean
water is assumed and a percent contribution to this amount due to
sediment removal as well. The potential increase in value is calcu-
lated and divided by the annual tons of sediment required to be removed.
Aesthetics The number of visitors to the area is estimated and a
proportion who are tourists is assumed. As a matter of national
pride to help reduce sediment in the Potomac, an amount per visitor
($.25 - $.50) is assumed as reasonable value to ascribe to
aesthetics. Based on this assumed value, the average amount of
damage per ton of sediment removed is calculated.
Flood Relief Incidental to Sediment Control The annualized flood
damages in the Potomac flood plain are estimated. The number of
impoundments necessary for sediment control is determined and their
flood prevention value in proportion to drainage area retained is
calculated. This is divided by the annual amount of sediment trapped
to yield a value per ton of sediment retained by impoundments.
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The damages to the users of the Potomac River below Seneca Creek
are summarized as follows in dollars per ton of sediment:
Metropolitan Water Supply .31
Electric Power 0.00
Dredging .67
Commercial Fishing 1.27
Recreational Fishing .88
Boating .84
Aesthetics 2.56
Subtotal 6.53
Flood Relief Incidental to Sediment Control .27
TOTAL 6.80
The Dow Chemical Company study meets criteria A, B, E, F, and G.
Existing data sources are used for calculating all damage estimates (A).
A specific pollutant, sediment, is addressed (B). Benefits are quanti-
fied in dollars (E). The distributional aspects of sediment control
are addressed in the identification of user groups who derive different
amounts of benefit from sediment removal (F). The methodology is of a
generalizable type which could be applied to other watersheds if compar-
able data were available (G).
Criteria C and D are not met. Despite the development of what
appear to be functions relating tons of sediment removed to benefits,
they are actually based on aggregate values and only assumed to be linear
(C). Judgment values and assumed values and proportions are also used
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in several of the user benefit calculations. The value measurement for
sediment removal is assumed to be equal to the dollar value of damages
caused by the sediment (D). This is a valid concept. However, particu-
larly for recreational fishing, boating and aesthetics, the dollar
values chosen are not necessarily reflective of the benefits derived
from the experience. Other problems with the analysis include the
neglect of possible higher equipment costs for electric power plants
and the cumulative impact of sediment on flooding.
5. Alan Randall, Berry C. Ives and Clyde Eastman, Benefits of Abating
Aesthetic Environmental Damage, New Mexico University Agricultural
Experiment Station Bulletin 618, Las Cruces, New Mexico, May 1974.
Randall et al evaluate the economic benefits to abating the
aesthetic environmental damage associated with the electric power indus-
try as perceived by users of the affected environment around the Four
Corners Power Plant, Fruitland, New Mexico. The study uses the theo-
retical concept of aggregate bids or benefits for the provision of a
public good as a basis for the analysis. Efficiency in the provision
of a public good can be achieved by equating the marginal bid with the
marginal cost.
The bidding game technique of data collection was adapted for use
in this study. The purpose of the games is to pose hypothetical questions
to measure the willingness of a sample of respondents to pay for envir-
onmental improvements. Five bidding games were developed to provide
several benefit estimates. Respondents were shown three sets of photo-
graphs depicting three levels of environmental damage around the power
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plant. The highest level of environmental damage was chosen as the starting
point and respondents were asked to respond yes or no to dollar amounts
to elicit the highest amount they would be willing to pay to improve
the environment to an intermediate level of damage or to minimal damage.
The following types of games were used: regional sales tax (air quality
region); additional charge to electricity bill to all who use the elec-
tricity produced by the plant even if they do not live in the region;
monthly payment (no particular payment vehicle); addition to user fee
for recreationists; compensation game which assumes that the respondent
owns the environment and accepts monthly rent from the industry to
damage the environment.
Determination of three points on the aggregate bid curve cor-
responding to the levels of environmental damage illustrated were
calculated by aggregation methods appropriate to the stratified random
sampling technique used. Marginal aggregate bid curves or price curves
were generated by taking the first derivatives of the aggregate bid
curves. Benefits of an intermediate level of aesthetic damage abate-
ment were estimated at $11 to $15 million annually, while benefits of
complete abatement were $19 to $25 million per year.
Calculation of the"income elasticity of bid" and the "electric
bill elasticity of bid" indicated that bids for abatement
were higher for households with higher incomes and for households con-
suming more electricity.
Questionnaire results suggested that financial arrangements
for abatement of aesthetic environmental damage from the power plant
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should place the burden on industry and consumers of electricity.
Criteria C, D, E, F and G are met by the Randall study. The
object of the bidding games are to produce willingness-to-pay measures
in response to changes in environmental damage (C). Water quality is not
considered in this study but the relationship between aesthetic environmental
quality and a value measurement is specifically addressed in the bidding
games (D) . Benefits are quantified in dollars (E). The distribution of
benefits is considered through sampling different groups including recre-
ationists and by investigating the elasticities of income and electric
bill (F). Also, the method of using alternative games elicited infor-
mation about the preferences for distribution of the financial burden for
abatement of pollution from the power plant. The data collection and
analysis methods were successfully used in this instance and could be
applied elsewhere, however, a new survey would have to be taken (G).
The Randall study does not meet criteria A or B. To use the
methodology tested in this study requires the development and execution
of a reliable survey (A). A specific benefit, aesthetics, is addressed
in this study, but the pollutants are many, including particulate
emissions, power lines and strip mining (B).
6. Thomas D. Crocker, Robert L. Horst, Jr. and William Schulze, Multi-
disciplinary Research in Environmental Economics; Two Examples,
paper prepared for the workshop on Multidisciplinary Research Related
to the Atmospheric Sciences, National Center for Atmospheric Research,
Boulder, Colorado, August 1977.
Crocker, Horst and Schulze discuss the valuation of atmospheric
visibility to illustrate the application of an economic value measurement
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to a phenomenon generally considered to be intangible. The area chosen
for study is the Four Corners regions around Farmington, New Mexico
where the unique nature of the extended atmospheric visibility is valued
as a public good.
The research approach chosen for the study was outlined as follows:
emissionsj-—^ambient concentrations ^scientific measurement of
visibility reduction
i
public's perception of
value method ^ visibility change
No complete dispersion model was available to establish the first
linkage between emissions and ambient concentrations. The second linkage
was formed by taking pairs of black and white color photographs of
identical scenes at the same time. The meteorological range represented
by the black and white photographs was derived from a companion study.
The third linkage was assumed to be one-to-one based on other research.
A survey was used to make the fourth linkage.
A sample of the population of Farmington, New Mexico was surveyed
and asked to choose which among three color photographs most accurately
represented the ambient conditions during a week in the summer. The
respondent was then questioned on how he spent his leisure time during
that week including both activities and expenses related to those acti-
vities. He was then asked regarding thee chosen activities, how he
would change his use of leisure time if conditions were as they appeared
in the other two photographs.
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The authors used the household production theory (product substi-
tution and unit prices per hour) approach to develop compensated de-
mand functions for visibility from the survey data on time budgets
and expenditures. Compensating income surplus for a reduction in
visibility was calculated to be about forty dollars a week (in 1976
dollars). This is a measure of what the individual would have to be
paid to tolerate reduced visibility.
The Crocker study meets criteria C, D, E and G. A demand function
for visibility is generated using the economic model developed in the
study (C). Although the study concerns air quality rather than water
quality, the relationship between personal utility and pollution levels
is specified in the research approach and the economic model (D).
Benefits are quantified in dollars (E). The study demonstrates that an
analytically sound implementable model can be constructed to value
aesthetic phenomena (G). However, the data necessary to implement
the model must be acquired empirically.
Criteria A, B, and F are not met. As just mentionned, the data
on which this method is based must be collected for each case to which
the model is applied (A). The research outline for the study indicates
that the relationship between emissions (specific pollutants) and ambient
concentrations was not specified because of the lack of a complete model
(B). If this type of model were available for use with the economic
model, then this criteria would be satisfied. The study does not address
the question of distributional impacts (F).
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7. S. D. Reiling, K. C. Gibbs and H. H. Stoevener, Economic Benefits
from An Improvement in Water Quality, prepared for the Office of
Research and Monitoring, U.S. Environmental Protection Agency,
Washington, D.C., January 1973.
Reiling, Gibbs and Stoevener test a methodology for estimating the
economic benefits accruing to society as a result of water quality
improvements and associated recreation increase at Klamath Lake, Oregon.
Benefits to the local economy are also estimated.
The demand model is based on two prices which determine the number
of visitor-days which recreationists consume, the cost of travel to the
site which does not vary with the length of stay and the on-site cost.
The methodology designates a critical level of these costs beyond which
the recreationist will choose not to recreate at the site at all. Cost
variables are expressed on an individual basis rather than for the recrea-
tion group. Travel costs include transportation, food expenditures, lodging,
camping fees and other expenses. On-site costs include lodging, camping
fees, equipment rentals, meals and miscellaneous expenses. Other
variables for the model are demographic characteristics of the recreationist,
income after taxes and site characteristics which include the size of the
lake and use-intensities for water-related activities. These last are
subjective variables reflecting low, medium and high use for fishing,
boating, etc. It is assumed that the level of these activities is depen-
dent on the water quality and other physical features of the lakes. It
is noted that it would be more satisfactory to specify the model with
respect to the biological and physical parameters of the lake directly;
but these data were not available.
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Survey data collected at Klamath lake and at three other nearby
lakes with varied characteristics are used to estimate equations of the
statistical demand model. Four relationships are estimated: the cri-
tical on-site cost, the critical travel cost, the demand relationship
and the number of visits relationship. The recreational value of each
lake was determined from the demand model by calculating the consumer
surplus which is a function of on-site costs, length of stay per visit,
travel costs and average income. The resulting per visit value was mul-
tiplied by the estimated number of visits to give a net economic value
for Klamath Lake for 1968 of $82,000. The relationship derived between
the number of visits to a site and the characteristics of the site was
used as a predictor for percent increase in visits to Klamath Lake if
water quality improved. New use-intensity ratings were hypothesized
for the lake given a hypothesized two-stage improvement in water quality.
The increase in visits based on the new use-intensity ratings was cal-
culated, and based on this increase, the new economic value was estimated.
The first stage of water quality improvement, removal of algae, would
yield $1.2 million worth of recreation benefits and the second stage,
lower water temperature and beach improvement, would yield an addition
$2.66 million.
The impact of expanded recreational use of Klamath lake upon the
local economy is estimated through the use of an input-output model of the
Klamath County economy. The model measures the gross flow of goods and
services between sectors. A sampling of the sectors of the economy were
surveyed to obtain the necessary detailed financial data for construction
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of the transactions matrix. Data from the demand model were used to
obtain total expenditures in Klamath County associated with recreation
by sector. The recreation expenditures are viewed as part of final
demand of the input-output model affecting total output and household
income. Regional recreation benefits for 1968 for Klamath Lake are cal-
culated from the input-output model to be $227,000 of household income.
The hypothesized two-staged improvement in water quality discussed
above would increase household income by $347,820.
The Reiling, Gibbs, Stoevener study meets criteria C, E, F and G.
They develop a demand function which is used to estimate the recreational
value of each lake studied (C). The input-output model is also based on
sound economic principles. Benefits are quantified in dollars and
secondary benefits to the local economy are also estimated (E). The
distributional aspects of the impact of water quality improvements are
addressed by the use of the input-output model which indicates which
sectors of the economy benefit from increased recreation expenditures
(F). The methodologies used in the study are applicable elsewhere,
although both the recreation survey used to provide data for the demand
model and the survey of the regional economy for the input-output model
would have to be carried out at each location studied (G). There would
also have to be agreement on the values assigned to the use-intensity
variables for the methodologies to be used in any comparative manner.
Criteria A, B and D are not met by the Reiling study. To implement
either of the methodologies used would require a survey data collection
effort although other study areas might have more readily available
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financial data for an input-output model (A). Water quality parameters
are not specified in the model (B). As mentioned earlier, the authors
feel that a more satisfactory model would relate changes in the physical
characteristics of the water resource to responses in human behavior
but that these data were not available (D).
8. Battelle Memorial Institute, "The Impact of Mine Drainage Pollution
on Industrial Water Users in Appalachia," Appendix A to Acid Mine
Drainage in Appalachia, a report by the Appalachian Regional
Commission, Columbus, Ohio, March 1969.
The Battelle Memorial Institute conducted a study to estimate the
effect of mine drainage pollution on the cost of water use by industry
in Appalachia. The impact on regional industrial activity was also
examined.
The study focused on the effect of mine drainage on production tech-
niques and production costs. The necessary data could only be obtained
by visits to industrial plants and by detailed interviews with plant and
company personnel. Sixty-seven in-plant interviews were conducted in
six river basins. The sample of plants to be interviewed was chosen to
pinpoint those industrial water users most likely to be affected by acid
mine drainage. This involved collection of data on the general water use
characteristics and water quality sensitivities of all major Appalachian
industrial water users. Other data collected included: the costs of
water utilization for water supplies, pumping, treatment, distribution,
recirculation and waste treatment; the proportion of water costs to the
overall value of industrial production; methods adopted by industries
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to adjust to mine drainage conditions; and costs of adjustments to mine
drainage.
The economic impact of acid-mine drainage was inferred from the in-
terview data. Detailed cost estimates were developed for various methods
of treating mine drainage polluted industrial water supplies, including
treatment at the source and lime neutralization. A hypothetical three-
stage reduction in mine-drainage pollution was assumed and treatment
costs were applied to interview data to obtain estimated savings. The
following costs and potential savings were investigated: costs of alter-
native water sources (savings from substituting raw surface water);
costs of using modified equipment; abnormal operation, maintenance
and replacement costs of production equipment or water-system components;
costs of product adjustment (savings in product quality control); costs of
treating mine-drainage derived contaminants in withdrawal of direct
supplies of water from mine-drainage rivers; costs of treating mine-drainage
derived contaminants in water purchased from municipal or other supplies
affected by acid-mine drainage. Expected changes in production were also
analyzed, including new levels of output, new location, new products,
new quality of output given reduced production costs resulting from
reduction in mine drainage. The results for the sample were then pro-
jected to include the entire manufacturing sector within each river
basin surveyed.
The survey showed the maximum savings from pollution reduction would
occur from treatment at the source rather than lime neutralization. The
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maximum possible savings from a 90 percent reduction in mine drainage
at the source in all Appalachian river basins is $1,230,000. The greatest
portion of the savings come from savings in chemicals used in conventional
methods of water treatment. The major savings would be to large plants
directly using river water. Fifty percent of the entire savings would
accrue to several very large steel producing plants in one region of
Pennsylvania. It was found that adjustments to acid-mine drainage accounted
for only a small fraction of total water costs at manufacturing plants
which themselves were generally less than one percent of the total value
of sales. The study concluded that no regional industrial impacts includ-
ing water use, production, employment and use of raw materials and power
would occur as a result of reduction in acid-mine drainage.
The Battelle study meets criteria B, C, D, E, F, and G. A specific
pollutant, acid-mine drainage, is the focus of this study (B). From the
survey data, functional relationships are developed showing the savings
resulting from various levels of pollution reduction depending on the
type of treatment employed (C). The detailed industry-by-industry in-
vestigative work done for this study was aimed at identifying the economic
impact of a specific pollutant on a specific receptor, the manufacturing
industry in Appalachia (D). Benefits of pollution reduction are quan-
tified in dollars (E). Distribution of the savings from mine drainage
reduction was considered for different industry groups and between large
and small industries (F). The methodology employed in this study can
be applied to other regions, and in many cases, is the only way to
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understand the financial impact on industry of environmental improve-
ments (G). It, of course, involves expensive detailed interviewing.
As just mentioned, because of the techniques necessary for data
collection for this type of study, application is not easy and therefore
it does not meet criteria A.
Summary of Reviews
This assessment of benefit studies has shown that few studies meet
all criteria. Criterion A, ease of application, proved to be the most
difficult criteria to satisfy. This is primarily because response to
changes in environmental quality is such a complex subject and there
are few relevant studies. Three of the studies summarized here do meet
criteria A: the Tihansky study, and the Dow Chemical Company study, and
the property value study by Day and Gilpin. In the Tihansky study, the
benefit group chosen, household water supply, and the pollutant, dis-
solved minerals, had generated enough research interest so that there
were data available on which to develop a damage function relating
pollutant to economic value. The Dow Chemical study used data (where
available) and judgment values where sufficient data were lacking.
Of the methods in the three Studies satisfying Criterion A, the
property value technique employed by Day and Gilpin is most appealing
because it relies on existing (secondary) data, either property tax
assessments or sales prices and census data. However, there are
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shortcomings to the approach, including the difficulties involved in
selecting a site for cross-sectional or time series study, where the
effects of changes in water quality can be isolated. (For example,
see discussion on pages 6 to 9 in Darroger and Dornbusch, 1973.)
problem with the property value approach is discussed by Binkley and
Hanemann. They note that if property values rise near a water body
they may fall in an area further away from the water body and simply
knowing how much property values change near the water body will not
allow conclusions regarding change in social welfare. (See S. Binkley,
W. Hanemann, Urban Systems Research and Engineering, Inc., pages
14-18.)
The failure of several studies to meet criterion D, pollution level-
value measurement relationship, points to a major problem in benefit
estimation. The lack of existing data that link pollutant and value
measurement results in the need to conduct surveys or undertake other
expensive data collection efforts. A study that requires primary data
collection to establish this relationship therefore does not meet cri-
teria A. Such empirical data for many water quality parameters, and
especially for interactions among water quality determinants, is not
readily available. Studies which do meet criteria D are the Tihansky,
Battelle Institute, Randall and Crocker studies. Both the Tihansky and
Battelle Institute studies are concerned with pollutants which affect
the cost of production, the former for the household and the latter for
industry and both are able to specify defensive expenditures for different
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levels of pollution. The Randall and Crocker studies specifically es-
tablish the connection between pollution levels and value measurement in
their surveys.
Criteria B, consideration of identified pollutants, is a third
area of difficulty with most of the studies considered. Only the
Tihansky, Dow Chemical Company, and Battelle Institute efforts address
specific pollutants (dissolved minerals, sediment and acid-mine drainage,
respectively). Other studies focus on more general types of pollution
such as lowered visibility, or rivers and lakes with poor water quality,
and do not develop data or methodologies to handle individual pollutants
or combinations of pollutants.
Criteria C, E, F and G (theoretical validity; benefit quantification;
distribution of impacts; and generalizability) are more readily met than
A, B or D. The Tihansky, Day and Gilpin, Randall, Reiling and Battelle
Institute studies satisfy these criteria. These studies are based on
accepted methodologies, and they quantify benefits in dollar terms. They
address the equity question in different ways, including comparing impacts
on different regions, different income groups, different industries or
sectors of the economy, or different population groups defined by location
or consumption. The techniques employed in these studies are reproduc-
ible in other locations for other problems, however, most would require
new data collection efforts. The Crocker study meets criteria C, E and
G but does not address the equity question. The Dow Chemical Company
and Oster studies satisfy criteria E, F and G but calculate aggregate
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benefits rather than developing a functional relationship between bene-
fits and levels of pollution.
From review of benefit methodologies presented here it appears
that there are several approaches for evaluating water quality impacts
from agriculture that could be developed for empirical testing. Table E-
2 shows which methodologies are most appropriate for particular activi-
ties, uses or groups. Referring back to the studies reviewed, examples
of methodologies applied to specific benefit categories include:
1) time budget - Crocker study of aesthetics;
2) bidding games - Randall study of aesthetics and Oster study of
recreation (a less sophisticated example where aggregate
willingness-to-pay data is collected);
3) travel cost - Reiling study of recreation;
4) marginal cost - Tihansky study of household water supply and
Battelle Institute study of industrial water supply;
5) net factor income - Dow Chemical Company study of commercial
fishing (among other things);
6) market study - Day and Gilpin study of property values;
7) non-dollar measurement - Day and Gilpin's value ranking
study and;
8) input/output model - Reiling model to estimate local economic
benefits.
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Table E-2. Comparison of Methodologies to Measure Water Quality Benefits
Methodology
Types
time
budget
bidding
games
travel
costs
marginal
costs
net factor
income
market
study
non-dollar
measurement
input/out-
put model
alternative
cost
Benefit Categories
01
aesthetic
X
X
ranking
e
recreatio
X
X
X
ranking
property
values
X
human
health
X
medical
costs
& lost
earnings
1-1
commercia
fishing
yield
change
x price
01
agricultur
yield
change
x price
2?
municipal
water suppl
treatment
production
costs
^ >i
industria
water suppl
treatment
production
costs
dredging
(navigatior
flood centre
X
ecology
change
in
habitat
cost to
reproduce
local or
regional
economy
X
-------
We have not reviewed a study devoted to valuing water quality benefits
to ecology (alternative cost); (for a good discussion of the sparseness
of the literature in this area, see Jordening, L., Development Planning
and Research Associates, Inc., pages 47-48).
As indicated in the above discussion, there are trade-offs involved
in choosing a methodology appropriate for use in estimating benefits to
water quality groups. The major one is the use of readily available
secondary data versus the need for a theoretically valid model which
relates specific pollutants to a value measurement. An example of this
tradeoff is the Dow Chemical Company study which resorts to judgment and
aggregate values, due to the lack of required data. There are more data
available for certain benefit categories such as household water supply
than for others such as aesthetics (see earlier discussion of Tihansky
study). Surveys are expensive and time consuming but there does not
appear to be any feasible alternative especially for measuring recreation
or aesthetic benefits which are two of the major categories in which bene-
fits from reducing nonpoint source pollution lie.
Another related problem is the need to isolate specific pollutants
and to relate them to a value measurement. Photographs are used in the
two studies concerned with air pollution (Crocker and Randall), a sedi-
ment load standard is developed in the Dow Chemical Company report, and
dissolved mineral concentration levels are specified in the Tihansky
study. These are examples of mechanisms employed to match a physical
measure of environmental quality to a measure of value to people. In
cases where more than one water quality parameter is of interest, as is
344
-------
often the case for water quality problems, the problem is much more
difficult. Again there is a trade-off between choosing a methodology
which develops a valid functional relationship and one which examines
benefits in the aggregate.
Several of the methodologies which we have reviewed can be used to
investigate the distributional aspects of water quality benefits. For
instance, bidding games can be applied to different population groups
defined by location or income, methods to evaluate the marginal cost
of treatment or production can be used to examine differences in bene-
fits among industry or household groups or among geographic regions, and
the input-output model may be used to focus on impacts to alternative
economic sectors. The major concern here, of course, is the definition
of equity, the decision to choose certain groups whose welfare is of
enough importance to require the focus of the study. As we have seen,
many groups are important depending on the region or problem of concern.
The land/water configuration and land uses of the study area be-
come important factors in determining the appropriate methodology(ies).
Is the water body a large flood control impoundment that is widely used
for recreation or is it a river used for municipal water supply and
industrial cooling water? Is it a small stream running through agri-
cultural land used by local sport fishermen or is it an estuary used as
a commercial fishery and for navigation purposes. These kinds of ques-
tions must be answered to determine which impact groups are likely to
derive the most benefit from improvements in water quality. Choice
of impact groups will in turn reduce the number of candidates for bene-
345
-------
fit methodology. If a number of beneficiary categories appear to be
important then several different instruments may have to be employed
simultaneously. This, of course, will increase the scope and expense
of a benefit study.
REFERENCES, APPENDIX E
Battelle Memorial Institute. The Impact of Mine Drainage Pollution on Indus-
trial Water Users in Appalachia, Appendix A. to Acid Mine Drainage in
Appalachia. A report by the Appalachian Regional Commission. Columbus,
Ohio, 1969.
Binkley, S. and Hanemann, W. Urban Systems Research and Engineering, Inc.,
The Recreation Benefits of Water Quality Improvement; Analysis of Day
Trips in an Urban Setting. Final Report. U.S. Environmental Protection
Agency, December 1975.
Crocker, T.D.; Horst, R.K. Jr.; and Schulze, W. Multidisciplinary Research in
Environmental Economics; Two Examples. A paper prepared for the Workshop
on Multidisciplinary Research Related to the Atmospheric Sciences, National
Center for Atmospheric Research, Boulder, Colorado, August 1977.
Darroger, S.N.; Dornbusch, D.N. Benefits of Water Pollution Control on Property
Values. EPA 600/5-73-005, Environmental Protection Agency, October 1973.
Day, J.C. and Gilpin, J.R. "The Impact of Man-Made Lakes on Residential Pro-
perty Values: A Case Study and Methodology Exploration." Water Resources
Research, Vol. 10, No. 1, February 1974.
Dorfman, R. and Dorfman, N. Economics of the Environment, W. W. Norton & Co.,
1972.
Dow Chemical Company. An Economic Analysis of Erosion and Sediment Control
Methods for Watersheds Undergoing Urbanization. Final report, U.S. Dept.
of Interior; Midland Michigan, February 15, 1971 - February 14, 1972.
Energy Resources Company, Inc. Analysis of Construction Grant Funding of
Wastewater Reclamation Projects. Interim Progress Report. U.S. En-
vironmental Protection Agency, Office of Water Programs Operations,
Municipal Construction Division, August 19, 1977.
Gibbs, K.C.; Reiling, S.D.; Stover, H.H. Economic Benefits from an Improve-
ment in Water Quality. EPA-Re-73-008. U.S. Environmental Protection
Agency, January 1973.
346
-------
Gramlich, F.W. Estimating the Net Benefits of Improvements in Charles River
Water Quality. Unplublished Ph. D. dissertation, Harvard University,
March 1975.
Jordening, L., Development Planning and Research Associates, Inc. Estimating
Water Quality Benefits. PB 245-071. U.S. Environmental Protection Agency,
Office of Research and Monitoring, August 1974.
Loehman, Edna. A Model for Valuing Health Effects of Air-Quality Improve-
ments . Preliminary Staff Paper 48, University of Florida, Institute
of Food and Agricultural Sciences, Food and Resource Economics Department,
Gainesville, Florida, April 1977.
Nathan, Robert R., Associates, Inc. Mine Drainage Pollution and Recreation
in Appalachia. The Appalachian Regional Commission. Washington, D.C.,
June 1969.
\
Oregon State University. The Demand for Non-Unique Outdoor Recreational
Services; Methodological Issues. Technical Bulletin 133, Agricultural
Experiment Station, Corvallis, Oregon, May 1976.
Oster, Sharon. "Survey Results on the Benefits of Water Pollution Abatement
in the Merrimack River Basin." Water Resources Research, October 1977.
Peskin, H.M.; Seskin, E.P. Cost Benefit Analysis and Water Pollution Policy.
URI 77000, Urban Institute, Washington, D.C., 1975.
Stoevener, H.H.; Reiling, S.D.; and Gibbs, K.C. Economic Benefits from an
Improvement in Water Quality. EPA-R5-73-008, U.S. Environmental
Protection Agency. Office of Research and Monitoring, Washington, D.C.,
January 1973.
Tihansky, D.P. "Damage Assessment of Household Water Quality," Journal of
Environmental Engineering Division, ASCE, Vol. 100, No. EE4, August 1974.
Unger, S.J. and Jordening, D.L. Bibliography of Water Pollution Control
Benefits and Costs. EPA 600/5-74-028, Environmental Protection Agency,
Washington, D.C., October 1974.
U.S. Department of Agriculture. Benefits of Abating Aesthetic Environmental
Damage from the Four Corners Power Plant, Fruitland, New Mexico. Bulletin 618,
Agricultural Experiment Station, New Mexico State University, Las Cruces,
New Mexico, May 1974.
Urban Systems Research and Engineering, Inc. The Recreation Benefits of Water
Quality Improvement, Analysis of Day Trips in an Urban Setting. Final
report. U.S. Environmental Protection Agency, December 1975.
347
-------
Appendix F
Crop Response to Fertilizer
One of the policies evaluated in Section 6 of the report pertains
to mandatory reduction in the use of fertilizer as a way to improve
water quality. This analysis provides the basis for estimating yield
reductions and farm revenue changes that are treated in Section 6.
To estimate the effects of fertilizer usage on farm revenues (as
well as water quality) it is necessary to relate application levels to
yields. Nitrogen and P_O are the fertilizers of primary interest.*
The work of Taylor and Fronberg (1) for Illinois appeared attractive
because optimum levels of nitrogen application are related to yield
(expressed as a percent of maximum yields attainable) for a range of
corn to nitrogen price ratios. Moreover, small differentials in yield
are estimated in the range where optimal results are anticipated,**
i.e., where marginal costs and marginal returns are equal. (Some other
data, developed expressly for Indiana available at the outset of work,
were considered inadequate because average statewide conditions are
treated, rather than specific counties or soil types relevant to the
Black Creek area (e.g., (4) and (5)). If the Illinois yield-nitrogen
* The K2O fertilizer is not analyzed because crop response and water
quality are less sensitive to potassium than to nitrogen and phosphorus.
** For example, Taylor and Frohberg list seven nitrogen application
rates which cover a range of corn yields from 100 percent of maximum
yield to 99.1 percent.
348
-------
response relationships could be made applicable to Indiana, we would be
able to investigate conditions where relatively large reductions in
nitrogen (e.g., 14 percent) applications result in small reductions in
yield (e.g., one percent).
Data on corn response to nitrogen for Indiana were then obtained
from Meta's field work (2, 3). Tests had been carried out for a range
of nitrogen applications from 0 to 180 pounds per acre on Blount Silt
Loam and 0 to 210 pounds per acre on Odell Silt Loam on the two differ-
ent soil types identified as relevant to Allen County (2, 3). However,
the test results are of limited value because only two intermediate levels
between zero and maximum nitrogen application are reported. A comparison
of Indiana data with Taylor/Frohberg (1) was made to see if the relation-
ship developed for Illinois could be applied to the Indiana Odell Silt
Loam soil and thus establish a more precise estimate of yield response to
nitrogen in the range of near maximum yielded conditions, i.e., where only
small yield reductions occur with sizeable reductions in nitrogen appli-
cation. Fig. F-l shows the comparison between Illinois crop response
(1) and that for Indiana on one type of soil (3). The four data points
provided by the Indiana tests (shown for three different applications of
P 0_) indicate a fundamental difference in the Indiana crop response
compared to Illinois. At low rates of nitrogen application (0 to 1.0
pounds nitrogen per bushel of yield), yield improvements are greater
on the Indiana soils than on the Illinois soils. Also it is seen that
maximum yield in Illinois occurs with 1.34 pounds nitrogen per bushel
349
-------
.2
.4 .6 .8 1.0 1.2
NITROGEN APPLICATION PER BUSHEL OF YIELD
1.4
1.6
1.8
Figure F-l. Comparison of Indiana Tests (1967-&9) to Illinois (Taylor-
Frohberg) in Corn-Nitrogen Response
350
-------
yield while maximum yields in Indiana occur at application rates be-
tween 1.43 and 1.67 depending on the level of P 0 application.* Thus,
*£ j
for maximum yield in Indiana on Odell Silt Loam soil of 130 bu/acre, the
Illinois response function would estimate a nitrogen application rate
of 174 Ibs/acre (1.34 Ibs x 130) whereas Indiana tests indicate 185.9
to 217.0 Ibs/acre are needed.
The yield response data from Reference (1) were therefore judged
unsuitable for Indiana Odell Silt Loam. However, the Illinois response
function was utilized in the subsequent steps for Odell Silt Loam as an
aid in approximating the general shape of the Indiana response function
because only four nitrogen application rates are reported from the
Indiana Tests. E'or the Blount Loam soil, the Illinois response function
was ignored; the yield response to nitrogen on Blount Loam soil is even
more divergent from the Illinois function than the Odell Silt Loam soil.
For the Odell Silt Loam (used for soil types 4 in Black Creek),
corn response for applications of 0, 70, 140 and 210 pounds of nitrogen
are reported for four different rates of P (i.e., 0, 17.6, 35.2, 52.8 Ibs
per acrs). Average yield over the period 1967 to 1969 is plotted as a
function of P for the four nitrogen application levels as shown in
Fig. F-2. A cross plot of yield versus nitrogen application was then
**
made for three specific rates of P~O as shown in Fig. F-3. Fig. F-3
£• O
* In Reference (1) phosphorous and potassium application rates are
assumed to be equal to the amounts removed in the grain which should
approximately maintain the P and K levels in the soil and thus the yield
response is essentially dependent only on the amount of nitrogen applied.
** Where P = .44 (P O ).
351
-------
150
140
130
120
7
7
no
100
90
80
litr
?gen
app
led
fib
.acr
0.
• 4P.
70
60
50
40
0 10 20 30 40 50 60
phosphorus applied (Ib/acre)
Figure F-2. Yield Response of Corn to Fertilizers (Odell Silt Loam)
352
-------
Ul
140
130
120
110
100
UJ
tc
u
g ^
a
uj
80
70
60
50
S14 ) BU> \CRE
TI vi n
FFOHB
ORVE
'MA
X^
RG
^
= KDLE
iOTt
OLf
-L
/ACF
met
/ACFi
20 40 60 80 100 120 140
NITROGEN APPLIED (LB/ACRE)
160
180
200 220
Figure F-3. Yield Response of Corn to Fertilizers(Odell Silt Loam)
-------
includes the Taylor/Frohberg response function which served as a guide
for interpolating between the four data points reported in the Indiana
tests. Nitrogen application of 160 Ibs/acre and P2O5 of 30 Ibs/acre
were recommended to achieve an expected yield of 130 bu/acre in the
Black Creek area.* This is in close agreement with the yields (131 bu/
acre) obtained from the P~0 crossplot and shown inFi
-------
(soil type 3) the recommended N is 160 Ibs/acre and 30 Ibs/acre of P-jO^
with yields in the Black Creek area expected to be 130 bu/acre. For
uplands (soil type 1) the recommended N is 140 Ibs/acre and 30 Ibs/acre
of P~05 for an expected yield of 120 bu/acre. From Fig. F-4 it is seen
that with N = 160 for lowland soils, the yield is the same as at N = 120,
the yield at other levels of nitrogen application is obtained by multi-
plying the response function value (i.e., percent of yield at 120 lbs/
acre nitrogen) by 130 bu/acre.
For upland soils at N = 140, Fig. F-4 shows that expected yield
is 1.022 times the yield at N = 120. Since we force the relationship
of 120 bu/acre yield at N = 140 to comply with Galloway's estimate,
the reference yield (at 120 Ibs/acre of nitrogen) must be reduced to
117.4 bu/acre (i.e., 120 bu/acre * 1.022).
All the above calculations for soil types 1 and 3 are based thus
far on the yield-nitrogen response data which are reported to a fixed
level of P Cv of 120 Ibs/acre. We next must adjust the derived yield-
-------
to obtain the yield-nitrogen response curves for P~0 = 30 and 20 respec-
tively. The final response curves shown in Fig. F-5 for soil types 1
and 3 have, therefore, been derived from Fig. F-4 but with adjustments to
incorporate applications of P_O and nitrogen to give the expected yields
recommended to Meta Systems (by Galloway) for soil types 1 and 3.
To investigate the sensitivity of water quality to various fertili-
zation levels based on the yield response relationships, changes in nitro-
gen and P_0 application were postulated and applied to the derived yield
£ 3
response functions. Decreases in nitrogen levels of 13 percent from rates
recommended by Galloway would be desirable according to Commoner (6) to
reduce nitrate concentrations for surface water for the East Central region
"
O
GO
9
8
>-
s
77
20 40 60 80 100
NITROGEN APPLIED (LB/ACRE)
120
140
160
180
Figure F-4. Yield Response of Corn to Nitrogen
Silt Loam, o = Data Points from Ref. 3.
120 Ib/acre). Blount
356
-------
of Illinois. Commoner indicates that if the rate of fertilizer application
were reduced to 146 kg of N per hectare corn (from a level of 168 kg of N
per hectare), the 10 ppm standard would be exceeded no more than five per-
cent of the time during the spring months.
In addition, two cases were postulated to evaluate the impacts on
yield from changes per acre reduction in nitrogen which is a lesser re-
duction than dictated in P_0 application to corn. The changes in the
recommended P O level were stipulated on an arbitrary basis. The
recommended levels of P-O^ were increased and decreased in 10 pound
increments. In preliminary studies, these changes in phosphorus fer-
tilization rates were found to have negligible impacts on water quality
and therefore were not considered further.
NITROGEN APPLIED (LB/ACRE)
Figure F-5. Yield Response to Nitrogen (Blount Silt Loam)
357
-------
REFERENCES, APPENDIX F
1. Taylor, C. Robert and Klaus K. Frohberg, "The Welfare Effects of
Erosion Controls, Banning Pesticides and Limiting Fertilizer
Applications in the Corn Belt," American Journal of Agr. Econ.,
February 1977.
2. Stivers, R. K., et al., "Nitrogen, Phosphorus and Potassium Ferti-
lization of Continuous Corn on Blount Silt Loam, 1962-1965,"
Department of Agronomy and AES Farms, Purdue University Agricultural
Experiment Station Research Progress Report: 299, March 1967.
3. Stivers, R. K., et al., "Response of Corn to Fertilization on Odell
Silt Loam, 1967-1969," Research Progress Report: 385, February
1971.
4. "Crop Yield Response to Fertilizer in the United States," U.S.
Department of Agriculture Statistical Bulletin No. 431, August
1968.
5. Spies, C. D., "Corn Fertilization," Agronomy Guide, Cooperative
Extension Service, Purdue University AY171.
6. Commoner, B., "Cost-Risk-Benefit Analysis of Nitrogen Fertilization:
A Case History," Ambio, 62-3, 1977.
358
-------
TECHNICAL REPORT DATA
(Please read Instructions on the reverse before completing)
. REPORT NO.
EPA-600/5-79-009
3. RECIPIENT'S ACCESSION NO.
4. TITLE ANDSUBTITLE
Costs and Water Quality Impacts of Reducing Agricultural
Nonpoint Source Pollution: An Analysis Methodology
5. REPORT DATE
August 1979 issuing date
6. PERFORMING ORGANIZATION CODE
. AUTHOR(S)
8. PERFORMING ORGANIZATION REPORT NO.
9. PERFORMING ORGANIZATION NAME AND ADDRESS
Meta Systems, Inc.
10 Holworthy Street
Cambridge, Massachusetts 02138
10. PROGRAM ELEMENT NO.
1BA609
11. CONTRACT/GRANT NO.
R805036-01-0
12. SPONSORING AGENCY NAME AND ADDRESS
Environmental Research Laboratory—Athens, GA
Office of Research and Development
U.S. Environmental Protection Agency
Athens, Georgia 30605
13. TYPE OF REPORT AND PERIOD COVERED
Final, 8/77-9/78
14. SPONSORING AGENCY CODE
EPA/600/01
15. SUPPLEMENTARY NOTES
16. ABSTRACT
This study addresses the problem of analyzing nonpoint source pollution impacts
from agriculture. A methodology for regional-level planning is presented that, with
further refinement, could prove of significant value for broad analyses of large num-
bers of policy alternatives, including best management practices. The analytical me-
thod developed allows the simultaneous examination of the water quality impacts of
selected agricultural practices and the economic effects that alternative practices and
nonpoint source pollution control policies have on the farmer. The nonpoint source pol
lution control problems that the methodology addresses are limited to those that are
amenable to solution by incremental on-farm adjustments for damage reduction. The pro-
posed methodology includes a farm model, a water quality model, and a qualitative ap-
proach for the assessment of the social and economic impacts of water quality changes
on downstream users. It may be applied to evaluate government nonpoint source pollu-
tion control policies and the effects of alternative agricultural futures. The method-
ology's use for these purposes is evaluated through an illustrative example based on
data from the Black Creek watershed in Northeastern Indiana and a synthesized down-
stream impoundment.
17.
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
b.lDENTIFIERS/OPEN ENDED TERMS
COS AT I Field/Group
Water pollution
Agricultural economics
Planning
Analysis
02B
68D
91A
18. DISTRIBUTION STATEMENT
RELEASE TO PUBLIC
19. SECURITY CLASS (This Report)
UNCLASSIFIED
21. NO. OF PAGES
367
20. SECURITY CLASS (Thispage)
UNCLASSIFIED
22. PRICE
EPA Form 2220-1 (9-73)
359
ft U.S. GOVERNMENT PRINTING OFFICE: 1979 -657-060/5381
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